{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb846718-6b9a-4b90-ae59-e4d464081a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据预处理完成，准备进行模型训练和测试。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('./combined_data.csv')\n",
    "\n",
    "# 删除不需要的列，例如时间戳或IP地址（假设你的数据集中有这些列）\n",
    "data.drop([' Timestamp'], axis=1, inplace=True)\n",
    "\n",
    "# 类型转换，将分类标签编码\n",
    "label_encoder = LabelEncoder()\n",
    "data[' Label'] = label_encoder.fit_transform(data[' Label'])\n",
    "\n",
    "# 检查并处理无穷大和非常大的数值\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)  # 将inf替换为NaN\n",
    "data.fillna(data.median(), inplace=True)  # 使用中位数填充NaN，确保之前中位数计算不包括inf\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(data.drop(' Label', axis=1))  # 确保标签列不参与标准化\n",
    "y = data[' Label']\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "print(\"数据预处理完成，准备进行模型训练和测试。\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e37ed3d0-1d91-4076-9dfe-29aedfbe9983",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 转换数据为torch张量\n",
    "X_train_tensor = torch.tensor(X_train.astype(np.float32))  # 确保数据类型为 float32\n",
    "y_train_tensor = torch.tensor(y_train.values.astype(np.int64))  # 先获取values再转换类型为 long (int64)\n",
    "X_test_tensor = torch.tensor(X_test.astype(np.float32))\n",
    "y_test_tensor = torch.tensor(y_test.values.astype(np.int64))\n",
    "\n",
    "# 创建数据加载器\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6212401c-3fda-4324-94b2-9abaf3f9aa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# 定义模型\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, input_size, num_classes):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.layer1 = nn.Linear(input_size, 64)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.layer2 = nn.Linear(64, 64)\n",
    "        self.output_layer = nn.Linear(64, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.layer1(x))\n",
    "        x = self.relu(self.layer2(x))\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型\n",
    "input_size = X_train.shape[1]\n",
    "num_classes = len(np.unique(y))\n",
    "model = NeuralNetwork(input_size, num_classes)\n",
    "\n",
    "# 损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc9a2783-0daf-439f-9156-e1b28e8bbf7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/200], Loss: 0.6843, Train Acc: 62.38%, Test Acc: 59.50%\n",
      "Epoch [2/200], Loss: 0.8461, Train Acc: 62.68%, Test Acc: 60.54%\n",
      "Epoch [3/200], Loss: 0.8376, Train Acc: 62.18%, Test Acc: 60.46%\n",
      "Epoch [4/200], Loss: 0.6336, Train Acc: 62.48%, Test Acc: 60.04%\n",
      "Epoch [5/200], Loss: 0.8013, Train Acc: 62.38%, Test Acc: 60.92%\n",
      "Epoch [6/200], Loss: 0.5960, Train Acc: 62.25%, Test Acc: 59.71%\n",
      "Epoch [7/200], Loss: 0.9119, Train Acc: 63.48%, Test Acc: 59.42%\n",
      "Epoch [8/200], Loss: 0.8993, Train Acc: 62.23%, Test Acc: 60.71%\n",
      "Epoch [9/200], Loss: 0.6921, Train Acc: 63.23%, Test Acc: 59.71%\n",
      "Epoch [10/200], Loss: 0.7252, Train Acc: 62.82%, Test Acc: 60.25%\n",
      "Epoch [11/200], Loss: 0.8242, Train Acc: 62.88%, Test Acc: 60.33%\n",
      "Epoch [12/200], Loss: 0.5485, Train Acc: 62.89%, Test Acc: 59.54%\n",
      "Epoch [13/200], Loss: 0.7638, Train Acc: 63.52%, Test Acc: 60.83%\n",
      "Epoch [14/200], Loss: 0.7582, Train Acc: 63.57%, Test Acc: 60.38%\n",
      "Epoch [15/200], Loss: 0.8015, Train Acc: 62.30%, Test Acc: 59.50%\n",
      "Epoch [16/200], Loss: 0.7939, Train Acc: 62.70%, Test Acc: 59.79%\n",
      "Epoch [17/200], Loss: 0.8308, Train Acc: 62.89%, Test Acc: 60.96%\n",
      "Epoch [18/200], Loss: 0.7271, Train Acc: 63.07%, Test Acc: 60.12%\n",
      "Epoch [19/200], Loss: 0.8307, Train Acc: 63.02%, Test Acc: 60.46%\n",
      "Epoch [20/200], Loss: 0.7474, Train Acc: 62.75%, Test Acc: 60.21%\n",
      "Epoch [21/200], Loss: 0.6655, Train Acc: 63.68%, Test Acc: 60.79%\n",
      "Epoch [22/200], Loss: 0.6584, Train Acc: 63.29%, Test Acc: 59.92%\n",
      "Epoch [23/200], Loss: 0.7050, Train Acc: 63.23%, Test Acc: 59.75%\n",
      "Epoch [24/200], Loss: 0.7077, Train Acc: 62.75%, Test Acc: 60.25%\n",
      "Epoch [25/200], Loss: 0.6608, Train Acc: 62.86%, Test Acc: 59.71%\n",
      "Epoch [26/200], Loss: 0.5711, Train Acc: 62.95%, Test Acc: 60.29%\n",
      "Epoch [27/200], Loss: 0.7233, Train Acc: 63.77%, Test Acc: 60.25%\n",
      "Epoch [28/200], Loss: 0.8431, Train Acc: 63.48%, Test Acc: 60.54%\n",
      "Epoch [29/200], Loss: 0.6869, Train Acc: 62.68%, Test Acc: 60.67%\n",
      "Epoch [30/200], Loss: 1.0450, Train Acc: 63.12%, Test Acc: 60.17%\n",
      "Epoch [31/200], Loss: 0.6889, Train Acc: 63.21%, Test Acc: 60.33%\n",
      "Epoch [32/200], Loss: 0.8615, Train Acc: 62.12%, Test Acc: 60.38%\n",
      "Epoch [33/200], Loss: 1.0628, Train Acc: 62.73%, Test Acc: 59.67%\n",
      "Epoch [34/200], Loss: 0.6395, Train Acc: 63.29%, Test Acc: 61.00%\n",
      "Epoch [35/200], Loss: 0.6923, Train Acc: 63.27%, Test Acc: 61.04%\n",
      "Epoch [36/200], Loss: 0.7463, Train Acc: 63.29%, Test Acc: 59.25%\n",
      "Epoch [37/200], Loss: 0.9084, Train Acc: 63.34%, Test Acc: 60.12%\n",
      "Epoch [38/200], Loss: 0.7136, Train Acc: 63.04%, Test Acc: 59.79%\n",
      "Epoch [39/200], Loss: 0.7133, Train Acc: 63.96%, Test Acc: 60.21%\n",
      "Epoch [40/200], Loss: 0.9814, Train Acc: 63.43%, Test Acc: 60.46%\n",
      "Epoch [41/200], Loss: 0.9694, Train Acc: 63.48%, Test Acc: 60.88%\n",
      "Epoch [42/200], Loss: 0.7033, Train Acc: 63.45%, Test Acc: 60.17%\n",
      "Epoch [43/200], Loss: 0.8041, Train Acc: 62.61%, Test Acc: 59.54%\n",
      "Epoch [44/200], Loss: 0.5818, Train Acc: 63.96%, Test Acc: 60.58%\n",
      "Epoch [45/200], Loss: 0.6548, Train Acc: 62.71%, Test Acc: 60.08%\n",
      "Epoch [46/200], Loss: 0.5836, Train Acc: 63.84%, Test Acc: 60.04%\n",
      "Epoch [47/200], Loss: 0.7682, Train Acc: 63.25%, Test Acc: 60.54%\n",
      "Epoch [48/200], Loss: 0.7202, Train Acc: 63.23%, Test Acc: 60.17%\n",
      "Epoch [49/200], Loss: 0.5757, Train Acc: 62.91%, Test Acc: 60.17%\n",
      "Epoch [50/200], Loss: 0.9567, Train Acc: 63.71%, Test Acc: 59.92%\n",
      "Epoch [51/200], Loss: 0.6973, Train Acc: 63.11%, Test Acc: 60.62%\n",
      "Epoch [52/200], Loss: 0.7133, Train Acc: 63.16%, Test Acc: 60.17%\n",
      "Epoch [53/200], Loss: 0.9892, Train Acc: 62.84%, Test Acc: 60.42%\n",
      "Epoch [54/200], Loss: 0.6609, Train Acc: 64.91%, Test Acc: 59.75%\n",
      "Epoch [55/200], Loss: 0.8267, Train Acc: 63.46%, Test Acc: 60.58%\n",
      "Epoch [56/200], Loss: 0.7770, Train Acc: 63.70%, Test Acc: 60.75%\n",
      "Epoch [57/200], Loss: 0.5946, Train Acc: 63.61%, Test Acc: 60.29%\n",
      "Epoch [58/200], Loss: 0.6572, Train Acc: 63.21%, Test Acc: 59.96%\n",
      "Epoch [59/200], Loss: 0.8394, Train Acc: 63.73%, Test Acc: 60.33%\n",
      "Epoch [60/200], Loss: 0.4506, Train Acc: 63.77%, Test Acc: 60.58%\n",
      "Epoch [61/200], Loss: 0.7225, Train Acc: 63.32%, Test Acc: 60.12%\n",
      "Epoch [62/200], Loss: 0.6754, Train Acc: 63.91%, Test Acc: 60.21%\n",
      "Epoch [63/200], Loss: 0.8145, Train Acc: 63.21%, Test Acc: 58.83%\n",
      "Epoch [64/200], Loss: 0.9971, Train Acc: 64.36%, Test Acc: 60.21%\n",
      "Epoch [65/200], Loss: 0.9180, Train Acc: 63.07%, Test Acc: 59.42%\n",
      "Epoch [66/200], Loss: 0.6547, Train Acc: 64.07%, Test Acc: 61.38%\n",
      "Epoch [67/200], Loss: 0.7209, Train Acc: 63.43%, Test Acc: 60.38%\n",
      "Epoch [68/200], Loss: 0.7364, Train Acc: 63.68%, Test Acc: 59.88%\n",
      "Epoch [69/200], Loss: 0.5437, Train Acc: 63.21%, Test Acc: 59.54%\n",
      "Epoch [70/200], Loss: 0.8160, Train Acc: 64.04%, Test Acc: 60.42%\n",
      "Epoch [71/200], Loss: 0.8490, Train Acc: 63.71%, Test Acc: 59.38%\n",
      "Epoch [72/200], Loss: 0.8613, Train Acc: 63.27%, Test Acc: 60.04%\n",
      "Epoch [73/200], Loss: 0.7895, Train Acc: 63.91%, Test Acc: 59.88%\n",
      "Epoch [74/200], Loss: 0.8170, Train Acc: 63.86%, Test Acc: 59.67%\n",
      "Epoch [75/200], Loss: 0.8273, Train Acc: 64.05%, Test Acc: 60.29%\n",
      "Epoch [76/200], Loss: 0.5516, Train Acc: 63.95%, Test Acc: 59.38%\n",
      "Epoch [77/200], Loss: 0.7895, Train Acc: 63.95%, Test Acc: 59.50%\n",
      "Epoch [78/200], Loss: 0.7319, Train Acc: 63.02%, Test Acc: 60.00%\n",
      "Epoch [79/200], Loss: 0.5774, Train Acc: 63.50%, Test Acc: 60.54%\n",
      "Epoch [80/200], Loss: 0.7047, Train Acc: 63.91%, Test Acc: 59.54%\n",
      "Epoch [81/200], Loss: 0.9631, Train Acc: 63.59%, Test Acc: 59.92%\n",
      "Epoch [82/200], Loss: 0.7156, Train Acc: 64.50%, Test Acc: 61.00%\n",
      "Epoch [83/200], Loss: 0.5144, Train Acc: 63.57%, Test Acc: 60.29%\n",
      "Epoch [84/200], Loss: 0.8027, Train Acc: 63.86%, Test Acc: 59.83%\n",
      "Epoch [85/200], Loss: 0.7253, Train Acc: 64.04%, Test Acc: 60.17%\n",
      "Epoch [86/200], Loss: 0.9247, Train Acc: 64.38%, Test Acc: 60.67%\n",
      "Epoch [87/200], Loss: 0.6615, Train Acc: 63.91%, Test Acc: 60.25%\n",
      "Epoch [88/200], Loss: 1.0069, Train Acc: 63.73%, Test Acc: 60.67%\n",
      "Epoch [89/200], Loss: 0.7518, Train Acc: 63.30%, Test Acc: 59.71%\n",
      "Epoch [90/200], Loss: 0.5511, Train Acc: 63.75%, Test Acc: 59.08%\n",
      "Epoch [91/200], Loss: 0.8040, Train Acc: 63.75%, Test Acc: 60.00%\n",
      "Epoch [92/200], Loss: 0.7548, Train Acc: 64.00%, Test Acc: 59.75%\n",
      "Epoch [93/200], Loss: 0.6306, Train Acc: 63.98%, Test Acc: 58.75%\n",
      "Epoch [94/200], Loss: 0.8185, Train Acc: 64.16%, Test Acc: 60.71%\n",
      "Epoch [95/200], Loss: 0.8974, Train Acc: 64.32%, Test Acc: 59.96%\n",
      "Epoch [96/200], Loss: 0.5982, Train Acc: 64.46%, Test Acc: 60.58%\n",
      "Epoch [97/200], Loss: 0.7329, Train Acc: 63.88%, Test Acc: 59.83%\n",
      "Epoch [98/200], Loss: 0.5879, Train Acc: 64.04%, Test Acc: 59.25%\n",
      "Epoch [99/200], Loss: 0.6470, Train Acc: 64.80%, Test Acc: 59.71%\n",
      "Epoch [100/200], Loss: 0.7451, Train Acc: 63.54%, Test Acc: 59.25%\n",
      "Epoch [101/200], Loss: 0.6710, Train Acc: 64.12%, Test Acc: 59.83%\n",
      "Epoch [102/200], Loss: 0.7126, Train Acc: 64.02%, Test Acc: 60.00%\n",
      "Epoch [103/200], Loss: 0.7457, Train Acc: 64.45%, Test Acc: 60.25%\n",
      "Epoch [104/200], Loss: 0.7320, Train Acc: 63.84%, Test Acc: 61.62%\n",
      "Epoch [105/200], Loss: 0.5713, Train Acc: 64.62%, Test Acc: 59.71%\n",
      "Epoch [106/200], Loss: 0.6436, Train Acc: 64.41%, Test Acc: 60.96%\n",
      "Epoch [107/200], Loss: 0.7034, Train Acc: 63.98%, Test Acc: 60.17%\n",
      "Epoch [108/200], Loss: 0.7506, Train Acc: 63.86%, Test Acc: 59.88%\n",
      "Epoch [109/200], Loss: 0.5327, Train Acc: 63.61%, Test Acc: 59.71%\n",
      "Epoch [110/200], Loss: 1.1094, Train Acc: 63.96%, Test Acc: 60.04%\n",
      "Epoch [111/200], Loss: 0.6076, Train Acc: 64.45%, Test Acc: 59.88%\n",
      "Epoch [112/200], Loss: 0.7620, Train Acc: 64.45%, Test Acc: 59.33%\n",
      "Epoch [113/200], Loss: 0.6129, Train Acc: 64.46%, Test Acc: 60.58%\n",
      "Epoch [114/200], Loss: 0.6210, Train Acc: 64.68%, Test Acc: 60.62%\n",
      "Epoch [115/200], Loss: 0.6208, Train Acc: 64.04%, Test Acc: 59.67%\n",
      "Epoch [116/200], Loss: 0.6208, Train Acc: 64.41%, Test Acc: 59.88%\n",
      "Epoch [117/200], Loss: 0.6831, Train Acc: 64.36%, Test Acc: 60.88%\n",
      "Epoch [118/200], Loss: 0.5567, Train Acc: 65.09%, Test Acc: 58.62%\n",
      "Epoch [119/200], Loss: 0.5019, Train Acc: 64.27%, Test Acc: 60.25%\n",
      "Epoch [120/200], Loss: 0.5099, Train Acc: 64.14%, Test Acc: 59.83%\n",
      "Epoch [121/200], Loss: 0.7868, Train Acc: 64.34%, Test Acc: 60.12%\n",
      "Epoch [122/200], Loss: 0.6802, Train Acc: 63.89%, Test Acc: 60.58%\n",
      "Epoch [123/200], Loss: 0.7749, Train Acc: 64.48%, Test Acc: 60.25%\n",
      "Epoch [124/200], Loss: 0.4399, Train Acc: 64.61%, Test Acc: 60.54%\n",
      "Epoch [125/200], Loss: 0.7483, Train Acc: 64.62%, Test Acc: 60.29%\n",
      "Epoch [126/200], Loss: 0.5478, Train Acc: 64.80%, Test Acc: 60.54%\n",
      "Epoch [127/200], Loss: 0.7811, Train Acc: 65.02%, Test Acc: 59.38%\n",
      "Epoch [128/200], Loss: 0.6211, Train Acc: 63.95%, Test Acc: 60.42%\n",
      "Epoch [129/200], Loss: 0.6346, Train Acc: 63.91%, Test Acc: 59.46%\n",
      "Epoch [130/200], Loss: 0.6116, Train Acc: 64.73%, Test Acc: 60.17%\n",
      "Epoch [131/200], Loss: 0.9936, Train Acc: 64.11%, Test Acc: 60.04%\n",
      "Epoch [132/200], Loss: 0.5566, Train Acc: 64.00%, Test Acc: 61.12%\n",
      "Epoch [133/200], Loss: 0.7629, Train Acc: 64.38%, Test Acc: 60.42%\n",
      "Epoch [134/200], Loss: 0.5942, Train Acc: 63.91%, Test Acc: 60.29%\n",
      "Epoch [135/200], Loss: 0.6383, Train Acc: 64.45%, Test Acc: 60.42%\n",
      "Epoch [136/200], Loss: 0.5839, Train Acc: 64.23%, Test Acc: 59.88%\n",
      "Epoch [137/200], Loss: 0.6690, Train Acc: 64.64%, Test Acc: 60.29%\n",
      "Epoch [138/200], Loss: 0.5791, Train Acc: 64.18%, Test Acc: 59.46%\n",
      "Epoch [139/200], Loss: 0.9761, Train Acc: 64.46%, Test Acc: 60.33%\n",
      "Epoch [140/200], Loss: 0.7319, Train Acc: 64.05%, Test Acc: 60.21%\n",
      "Epoch [141/200], Loss: 0.8215, Train Acc: 64.23%, Test Acc: 59.25%\n",
      "Epoch [142/200], Loss: 0.6731, Train Acc: 64.54%, Test Acc: 60.00%\n",
      "Epoch [143/200], Loss: 0.6568, Train Acc: 64.73%, Test Acc: 60.12%\n",
      "Epoch [144/200], Loss: 0.6574, Train Acc: 64.05%, Test Acc: 60.71%\n",
      "Epoch [145/200], Loss: 0.6647, Train Acc: 64.39%, Test Acc: 60.25%\n",
      "Epoch [146/200], Loss: 0.6832, Train Acc: 64.04%, Test Acc: 59.71%\n",
      "Epoch [147/200], Loss: 0.5876, Train Acc: 64.64%, Test Acc: 60.21%\n",
      "Epoch [148/200], Loss: 0.9073, Train Acc: 64.96%, Test Acc: 59.17%\n",
      "Epoch [149/200], Loss: 0.6703, Train Acc: 64.64%, Test Acc: 59.79%\n",
      "Epoch [150/200], Loss: 0.7215, Train Acc: 64.48%, Test Acc: 61.00%\n",
      "Epoch [151/200], Loss: 0.6859, Train Acc: 64.21%, Test Acc: 59.17%\n",
      "Epoch [152/200], Loss: 0.9042, Train Acc: 64.45%, Test Acc: 60.54%\n",
      "Epoch [153/200], Loss: 0.4540, Train Acc: 64.16%, Test Acc: 60.50%\n",
      "Epoch [154/200], Loss: 0.7981, Train Acc: 64.52%, Test Acc: 60.67%\n",
      "Epoch [155/200], Loss: 0.7849, Train Acc: 64.70%, Test Acc: 59.83%\n",
      "Epoch [156/200], Loss: 0.6671, Train Acc: 64.88%, Test Acc: 60.79%\n",
      "Epoch [157/200], Loss: 0.6185, Train Acc: 64.86%, Test Acc: 60.04%\n",
      "Epoch [158/200], Loss: 0.8419, Train Acc: 65.59%, Test Acc: 60.42%\n",
      "Epoch [159/200], Loss: 0.5554, Train Acc: 64.18%, Test Acc: 60.00%\n",
      "Epoch [160/200], Loss: 0.8071, Train Acc: 64.46%, Test Acc: 60.50%\n",
      "Epoch [161/200], Loss: 0.8048, Train Acc: 64.23%, Test Acc: 59.08%\n",
      "Epoch [162/200], Loss: 0.8172, Train Acc: 64.23%, Test Acc: 59.79%\n",
      "Epoch [163/200], Loss: 0.5824, Train Acc: 64.27%, Test Acc: 60.12%\n",
      "Epoch [164/200], Loss: 0.8044, Train Acc: 64.36%, Test Acc: 59.96%\n",
      "Epoch [165/200], Loss: 0.6955, Train Acc: 64.64%, Test Acc: 59.33%\n",
      "Epoch [166/200], Loss: 0.8160, Train Acc: 64.23%, Test Acc: 60.04%\n",
      "Epoch [167/200], Loss: 0.6037, Train Acc: 64.54%, Test Acc: 59.54%\n",
      "Epoch [168/200], Loss: 0.7541, Train Acc: 64.66%, Test Acc: 59.75%\n",
      "Epoch [169/200], Loss: 0.7495, Train Acc: 64.36%, Test Acc: 60.08%\n",
      "Epoch [170/200], Loss: 0.6178, Train Acc: 64.89%, Test Acc: 60.08%\n",
      "Epoch [171/200], Loss: 0.7246, Train Acc: 64.38%, Test Acc: 60.00%\n",
      "Epoch [172/200], Loss: 0.7781, Train Acc: 64.12%, Test Acc: 60.58%\n",
      "Epoch [173/200], Loss: 0.6731, Train Acc: 64.57%, Test Acc: 59.79%\n",
      "Epoch [174/200], Loss: 0.6907, Train Acc: 64.62%, Test Acc: 60.08%\n",
      "Epoch [175/200], Loss: 0.5495, Train Acc: 65.20%, Test Acc: 59.88%\n",
      "Epoch [176/200], Loss: 0.6790, Train Acc: 64.38%, Test Acc: 59.88%\n",
      "Epoch [177/200], Loss: 0.6250, Train Acc: 65.57%, Test Acc: 60.21%\n",
      "Epoch [178/200], Loss: 0.6443, Train Acc: 65.02%, Test Acc: 60.17%\n",
      "Epoch [179/200], Loss: 0.6819, Train Acc: 64.27%, Test Acc: 59.75%\n",
      "Epoch [180/200], Loss: 0.6541, Train Acc: 65.32%, Test Acc: 59.88%\n",
      "Epoch [181/200], Loss: 0.8135, Train Acc: 65.34%, Test Acc: 59.88%\n",
      "Epoch [182/200], Loss: 0.7519, Train Acc: 64.57%, Test Acc: 59.71%\n",
      "Epoch [183/200], Loss: 0.7661, Train Acc: 65.27%, Test Acc: 59.54%\n",
      "Epoch [184/200], Loss: 0.9349, Train Acc: 64.71%, Test Acc: 60.50%\n",
      "Epoch [185/200], Loss: 0.5642, Train Acc: 64.95%, Test Acc: 58.42%\n",
      "Epoch [186/200], Loss: 0.7048, Train Acc: 64.32%, Test Acc: 59.54%\n",
      "Epoch [187/200], Loss: 0.5634, Train Acc: 64.71%, Test Acc: 59.58%\n",
      "Epoch [188/200], Loss: 0.6736, Train Acc: 64.36%, Test Acc: 59.50%\n",
      "Epoch [189/200], Loss: 0.4431, Train Acc: 64.96%, Test Acc: 60.21%\n",
      "Epoch [190/200], Loss: 0.5866, Train Acc: 64.73%, Test Acc: 60.04%\n",
      "Epoch [191/200], Loss: 0.7119, Train Acc: 65.20%, Test Acc: 59.71%\n",
      "Epoch [192/200], Loss: 0.8430, Train Acc: 64.86%, Test Acc: 60.12%\n",
      "Epoch [193/200], Loss: 0.5390, Train Acc: 65.04%, Test Acc: 59.50%\n",
      "Epoch [194/200], Loss: 0.8009, Train Acc: 65.11%, Test Acc: 59.88%\n",
      "Epoch [195/200], Loss: 0.5660, Train Acc: 64.66%, Test Acc: 60.21%\n",
      "Epoch [196/200], Loss: 0.8997, Train Acc: 64.34%, Test Acc: 59.12%\n",
      "Epoch [197/200], Loss: 0.5001, Train Acc: 65.16%, Test Acc: 60.58%\n",
      "Epoch [198/200], Loss: 0.6620, Train Acc: 64.96%, Test Acc: 58.96%\n",
      "Epoch [199/200], Loss: 0.6970, Train Acc: 64.88%, Test Acc: 60.33%\n",
      "Epoch [200/200], Loss: 0.5584, Train Acc: 65.02%, Test Acc: 59.83%\n"
     ]
    },
    {
     "data": {
      "image/png": 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O/KGolR/ln6XCSzl1QkBTBuF1vHuQ54508/LxXuaWF3DbG1emfZ0mO7Tw0mg0Go1mHHQMJEryzFbhpUKNi6uL0oYa2/uDXPndrcQNcDoErjwHX7h2BUKIqR7qaYPO8dJoNBqNZhx0mmHGovy8WR9qXNNQRkvfELGU1kdt/QHiBnzvnev53DUrCERiDIYTMziHwlEOd4ytpuSZjhZeGo1Go9GMgw6fFF6La4pnseMlx71mbhmRmEH7QDDpea/piC2sKqK2VBb77rS95n+fO851//UcoejsLKcxHWjhNU56enrYsGEDGzZsYM6cOTQ0NFj/h8OjVwDeunUrL7zwwoivueGGGzj//PMna8gajUajmURUqHFxTdGsdrwcAlbWy9JCTT2DSc/3D0nhVV7goqZYtp/r9CVCrCd6hghG4lbrJM3oaOE1Tqqqqti9eze7d+/mYx/7GP/wD/9g/Z+fnz/q+0cTXl6vl507d9Lf35/TGYMTKeyq0Wg0ZzKdAyGK3U7qSj2zppzEVx/YywunEmMdCEQo8bhorJIt41LzvLxmD8rywvyE42UTXmpW52TNiAxGYrz359vY0+KdlOXNRLTwmkR27tzJZZddxqZNm7jmmmtoa2sD4Ic//CGrVq1i3bp1vOtd7+LEiRP8+Mc/5nvf+x4bNmzg2WefHbasP/zhD1x33XW8613v4u6777YeP3LkCFdddRXr16/n7LPP5ujRowB861vfYu3ataxfv57bbrsNgC1btqAKz3Z3d9PY2AjI9kXXX389b3nLW7jyyivx+/1ceeWVnH322axdu5YHHnjAWt+vfvUr1q1bx/r163nve9+Lz+dj0aJFVsPvgYGBpP81Go3mTKHDF6S21E2x20koGicSi4/+pmkkFje4e3szr3QkhJcvGKXE46S+zIPTIYYJKBVqLPU4qS2RwqvLJrza+qXwmqwaYPvbBnj2cDcPvnpqXO//055TPH2oa1LGkiv0rMZJwjAMbr31Vh544AFqamr47W9/y5e//GX+93//l3//93/n+PHjuN1uvF4v5eXlfOxjH6O4uJjPfvazaZd311138dWvfpW6ujpuuukmvvSlLwFwyy23cNttt3HjjTcSDAaJx+M89NBDPPDAA2zbts3q1zgar7zyCs8//zwLFy4kGo1y3333UVpaSnd3N+effz7XX389+/fv5xvf+AYvvPAC1dXV9Pb2UlJSwpYtW/jzn//MDTfcwN13383b3vY2XC7XpO5PjUajmel0DgSpLXFTZPY3HAxFKS8cPeIxXXQMBAnH4gyEE57LQDBCqceFM89BQ0UBJ3uTQ4beoQglHifOPAdlBS7y8xx0+hI5Xh2m8JqsdkNHO/0A7DrpHdf7v/voIcLROM9+/nIcjpk58/L0EF4P3Qbtr43rrQWxKOSl2Q1z1sIb/z3r5YRCIfbu3cvVV18NQCwWo76+HoB169Zxyy23cMMNN3DDDTeMuqyOjg4OHz7MxRdfjBACl8vF3r17WbhwIa2trVbfRo9Hxtsff/xxPvjBD1JYWAhAZWXlqOu4+uqrrdcZhsGXvvQlnnnmGRwOB62trXR0dPDkk0/yjne8g+rq6qTlfvjDH+bb3/42N9xwA7/4xS+4/fbbs95PGo1m/PQHIpQV6IucmULHQIiNC8opdsv+tP4ZLryUKzUQSsxcHAhEKS2Q58AFlYXDHK/+QITyQvmdE0JQU+Kma0D1p4ziM3PbMtUAGytHuqTweq21n0gsjisvOTD38N52Htrbxvdu3pBWWHX7QvhCUV461sOFS6snZUyTTU5DjUKIciHEvUKIg0KIA0KIC4QQXxNCtAohdpt/b8rlGKYKwzBYvXq1lef12muv8eijjwLw5z//mY9//OO88sornHPOOaPmVf3ud7+jr6+PRYsW0djYyIkTJ7jrrrvGPCan00k8Lq3vYDB5pkpRUZF1/84776Srq4udO3eye/du6urqhr3ezkUXXcSJEyfYunUrsViMNWvWjHlsGo1mbJzsGeLsf3mMHSdGd7Q1uUdVra8r9VDslsJkpjfKbjYT4PvDNuFlOl4A8ysLOZmSXO8dClNekBCTNSVuuvxSeKkZkEJMXqhROV6haJyDbcllKv7wSgt/d+dOHth9Km1OWTASs4TgPTtbJmU8uSDXjtcPgIcNw3i7ECIfKASuAb5nGMZ3Jm0tY3CmUglMUqNot9tNV1cXL774IhdccAGRSIRDhw5x1lln0dzczOWXX87FF1/M3Xffjd/vp6SkhIGBgbTLuuuuu3j44Ye54IILADh+/DhXXXUV3/zmN5k3bx73338/N9xwA6FQiFgsxtVXX83Xv/51brnlFivUWFlZSWNjIzt37uTcc8/l3nvvzTj2/v5+amtrcblcPPXUUzQ1NQFwxRVXcOONN/LpT3+aqqoqa7kA73vf+3j3u9/NV77ylQnvO41GMzpHu/zE4gaHO/1sbhzd1dbkloFAlFA0boYaleM1s3NdlTgKRKVI8bjyGAhEKDVd1IWVhfQNRZLEmNfmeAHUlrhpMsOKKsy4oq6Ek71DGIYx4cKqRzr9rJ9Xxqst/exq7mPtvDIA7t5+ki/e9xqLqoo41j3I4U4/jdVFSe/tNgVhWYGLh/a28fW3rqbEM/Mc4pw5XkKIMuBS4OcAhmGEDcPw5mp9043D4eDee+/lC1/4AuvXr2fDhg288MILxGIx3vOe97B27Vo2btzIJz/5ScrLy7nuuuu47777hiXXnzhxgqampqQyEosWLaKsrIxt27Zxxx138MMf/pB169Zx4YUX0t7ezrXXXsv111/P5s2b2bBhA9/5jtS0n/3sZ/nRj37Exo0b6e7uzjj2W265hR07drB27Vp+9atfsXKlbAexevVqvvzlL3PZZZexfv16Pv3pTye9p6+vj7/6q7+a7F2p0WjSoGaPddsSmyeLvsEwX/zDa0l9+zQjo2p41ZV6KPFID8M/4x2vhEvUMyhnK/qCUUtkLaiU6Sr2fK3+oeTwdk2J28rxUon15y6qZCgcs5Y5XkLRGCd7h7hseQ01JW4rz6vVG+DL9+/lkmU1/PZvpCFxKE3R1m6/XP/7LlhIMBLnz3vakp4fCEa46Ucv8Hrv9H5OuXS8FgFdwC+EEOuBncCnzOc+IYR4H7AD+IxhGH05HEfO+drXvmbdf+aZZ4Y9/9xzzw17bPny5ezZs2fY442NjbS2tg57/JVXXrHuP/nkk8Oev+2226zZjIqVK1cmreMb3/gGAB/4wAf4wAc+gM8nv7jV1dW8+OKLw5YJ8P73v5/3v//9abfp7W9/O+Xl5Wnfp9FoJhdVM0pd1U8m9+xs5q7tJ3nD6jouX1E76cs/Hek0Pw97cr1/hpeUaLElznf7Qswp9eALRS3h2FBRAEhBtaZBOk3DHS8PfUMRwtG4FWo8p7GSX73YxMneIaqL3eMe34nuIeIGLKktZuP8cnadlNLgrm0nMQyDf71xDTUlburLPBwxQ5J21GzLq86q4y+vtXHvzhbede4C6/mfP3ucnU19XDfXM+4xTga5FF5O4GzgVsMwtgkhfgDcBvwX8C+AYd5+F/hQ6puFEB8FPgpQV1fH1q1bk54vKyuzhMNEiMVik7Kc2ch4t/2zn/0sjz32GPfee++Y3h8MBod9jtOJ3++fUeOZavT2z67t3/W6PKnsP97C1q2ZHexsSN3237wgT8gv7dyDaDs95lwB/OVYmGAM3rYsOeF9Mj7751ulO3jiwKuo/O+de/ZS1Pv6hJabS460D1FfJGgbNHjqxR20VMgQaWdrE1u3nqIvKHOCn9uxB1eni7hh0DcYpr+zja1bewDoa5Pb/eBjW3nleIRCJ/iaDwLw8HM7GZjrJG4YdAcMagtHDqr5wgb/tSvIB9e4mVPkYHu7FK7ekwcpi8Y40RPhvoef5FcvBFhfk8eRV7dzBKhyRXjlaBtbt3qTlvd8sxzbkb2vsKkiyu8ODfKLB55gUVke/rDBT54eYlNdHtV5gWn97efyF9YCtBiGsc38/17gNsMwOtQLhBC3A39K92bDMH4K/BRg8+bNxpYtW5KeP3DgwKTkZvkmKcdrNjLebf/JT34yrvV5PB42btw4rvfmgq1bt5L6vTqT0Ns/u7b/100vA52IgjK2bLlgQsuyb/vx7kFOPLwVgLmNS9lyYeOElj2T+NKLT+APRfn+X1+WNANuMj77/VuPwGuvc93VlxKOxuHpx2hoXMqWixdNcNS5IRSN0ffIw7z97Hncs7OF+kXLWbekGp54irPXrGTL5vlEY3E+/fRDlNcvZMuW5QwEIxiPPMr6s5ay5ZLFAET3d/DLfTtYsuZs8rqOMK9qkLddczFfeu5hiusWsmXLMu548QT//Nh+tn3pSqpGcMCeOtjJ60++zAnHXN61ZSWvPn4YIQ5x87VbWNHi5Z5DL/FsfyUD4Vb+/i2buGx5DQDP+vfz65eauOTSy8izfa6vPXEY9h3izVddxhXhGM/+8Flu3w8PfOICfvbsMULxY/zbuy/i1IGd0/rbz1mOl2EY7UCzEGKF+dCVwH4hRL3tZTcCe3M1Bo1mouxt7eeCf3uC3gnmLmg0EyVXocYHXz2Fyof2Dp0+OV6nvAFO9QcZCEY5nCYsNVE6B0KUeJwU5juT6nhNJae8AfafSj9JK5XWvgCGARsWlAMyH6pfFUc1c7iceQ6qivKtHC7VLsie46Wq13f5QtasTo8rj7pStzXT8MFX24jGDU6MUturxSud1kf2tWMYBke6/DSUF1CQn8e6eWU4BPxhVysLqwq5xFYaYnldMaFonNaUNkXdfvmZeFx5VBTlc/v7NtM3FOGv/+9l/u/FE9ywoYHlddNvtOS6cv2twJ1CiD3ABuBfgW8LIV4zH7sc+IfxLtwwjNFfpJkRzNbP6lCHj7b+4KRNldZoxksukusNw+CPr57inMZKit1O60R8OrCjqc92f3JKcBzr8vPen2/jX/60n10n+6grlblCrjwHbqdjyoXXvz10kI/8akdWr1WlJJbWFFPglMJJtTkq9STncKn8NSXE7bXJaktUv8Yg7QNB5pj7QNUA6/KFeNnc36e8I/dvVMLpuDlL8Winn6W1xQAU5jtZMacUgPectzDJsVxaK8VTaoJ9tz9Mjc1hW9NQxndvXs+eln4iMYNPXblslL00NeQ0mG8Yxm5gc8rD752MZXs8Hnp6eqiqqprw9FVNbjEMg56eHqvg62wiGJE5D7O1Aa7m9CAWN+j2h8jPczAQjBKKxnA78ya83IPtPo50+vmXG9bQ2hfAGzh9nN2dJ3opzM+jMD+PnSf6uOW8hRNe5k+ePsZLx3rYdryXcDTOlhU11nPFbqdVQ2qqONblp9UbIBCOUZA/8vdBXTzOryykNF/Q7Q9Zs1hVAVWQjpbqxaiEuD25vqo4HyGgzRukyxdiTpkSXkW8cLSbxw90oK6zRxNeLX1DlBe68A5FeOi1do51+7lwSZX1/LmNFRzv9vP2TfOS3resToqzw51+rlpVZz3e5Q9RXZIc2nzT2nq+/fZ1RGLxYeUnpotZm0U5b948Wlpa6OqaWE+mYDA4KwXBZDCV2+7xeJg3b97oL5xhBCJy2rFfC69ZT99gmIFghIVVM+PgOxZ6/CHihjzh7Ds1QI8/zNzyggkv94+vniLPIXjTmjncvf2kFVo6HdjR1MeG+eWUeJxJ7td46R+K8MCrrbx903y+dv0q9rb201BeaD1f7HFO6QWaYRhWPa0TPYOcVV864uub+4Zw5QnqSj2UuU3hZfVhTK7TdaBNhi+VEC+3hRpdeQ4qC/PZ3zZA3MAmvAr5w64gD+yWocHewfDojpc3wKr6UoKRGL/e1kQwEmeJ6XgBfPoNK3jvBQupKEqeHFHqcTGn1MPhYY5XiLPmDN8PN2+eP+I4pppZK7xcLheLFk08iXHr1q0zKuF7KjmTtz1bgqbwGgxr4TXb+fYjB9l2vJcnP7NluocyZlR+1+q5pew7NUC3PzRh4RWNxXlgVysXL62mqthNWYHrtAk1DoaiHGgb4BOXL6XE4+KRfR10+oJWmGw83PtKC8FInPeevxC3M49NC5OL2BblO6e0nETvYNi6IGzKQni19AaYV1FInkNQmi/o8oUYyBBq7PKFiMUNK9SY2qaqpsTNnpZ+gESosaoAw4CXjvXy0UsX88yhLlqzCDVetryGZXXF/Otf5MzIpTbhVVbgytgia1ld8bDcvW5fiOqlM7dlkyLXOV4azawmZDleM7swomZ0TvYOWT3mZhsqv2v1XFlbaTIS7J882Mmp/iDvPk/WOSovdOE9TYTX7mYvcQM2NVayqbECgJ0nxu96xeMGv36pic0LK1g1N73AKfY4p9QZt/dGPN49eg7qyd4h5pl1ukrdgm5/2HK8ij0JD6au1E3cgJ7B0LDke0VNidv6DtbZcrwU16yeQ0N5Aa3ezK3nQtEYnb4QDRUFXLN6jvX40prijO+xs6y2hCOdfuJxw1reQDA6oTpiU4UWXhrNCASjOsfrdKFzIIQ/HLUO1LMJVSV9tXnS75qEBPs7XmqivszDlStlwdSygvyMsxof2N3Kl+97bcLrnAjPHu7iQ798mVgWn9+OE30IARsXlLNmbhlup2NC4cbnj3ZzvHuQ916QOU+s2O2cUme8yeypKETi/kg09w0x3xRHZfmC/kCEnsEQJW5nUkmGGpU8PxDCOxSmwJWHx5WcP2Z3Du05XvI5Nxvnl9NQUZAUajQMgxPdiXG2maKsobyAhVVFrJxTQlVR/rCwYiaW1RUTiMQsV63HrFqfmuM1E9HCS6MZASvUqIXXrKfLH8IwwD8Lw8YdAyGEgJVmOEm1Rhkv7YNxnj3czbvPXYDTrP4pQ43htDOQH93XwT07WqZVtP7Xk0d48mBnVqJzR1MvK+pKKPW4yHc6WD+vfELC644Xm6gqyufaNXMyvqbYPbWhxqaeIYSANXPLON49svDyBSN4hyLMr5DCqzRfCq0T3UPD3Cx7uQjvUHLV+tTXuPIEleaMx+rifKqK8nnT2nocDsHc8gL6AxHLBXz6UBdbvrOVfadkiFIJpnnmmL76llV85S2rst7+5WaCvZrZqBw47XhpNLOcoE6uPy0IRWOWm+ObYW1dnjrYyfn/+sSIfRI7B4JUFbkpdjspdjsn7Hg91RzB6RC889xE0nF5oYtIzLAmlNjp8ocIx+J0D05PqPZkzxDbjssSBW39I+cNxeIGu0562bSwwnpsU2MF+1r7CYTHnjJwyhvg8QMdvPOc+SPOJC1yO3OaknCk05dUPqGpZ4i5ZQUsryvhxCiOV7PZKmh+ZSLUCHJWZIknOdW71nSMOgaCeAORtDlW6jW1JR6rzIMQgj9/8hJue6Ps9atyEJXr9YopfHeYId8Ws2+kCn9euLSaGzY2jLIXEqiSEirPKyG8dI6XRjOr0eUkTg/sQsU3jkbQhtk6JRf8+qUm2geCHO/KfPKUhSrlyc6eXzMeBkNRnmuNcs2aOUkhIzVzLV24Ua0vtWDlWPnF88e548UTY37fva+0WPfb+zPnDYF0QPyhKJsbE8Jr88IKonHD6v03Fu7afhIDrFy4TJR4nPhD48+RO9Th4xO/ecW62LPjC0b4q9u38cm7dlmPNfUMsqCykMaqQjoGQgyN4OSq5tjK8SozHa9T/cGkxHqQ3y+ATl+I/gyOl3pNfVnyZIU5ZR4rLNlQLp9TztZ+c6bkqy1e+XhfAIdIhCrHSlmBi/oyj1VAtttnhhq146XRzG4CVqhRJ9fPZuzCazzhoBeO9nDONx/nYHt2VcKzxTsU5pnDsiTOSE5Opy9kJTFXF+ePS3jdu7OFv/rpS2z6xmMMRuB95yfnK5WNJLzM/XdqhGTpbPj5c8f55QsnxvSeeNzg9ztbWGs2bW4bRXgpB8Q+y++8xVWUuJ38ZvvJMa07HI1z1/ZmrlxZa4XEMlGU7yQYiRONxce0DsXvXm7mT3varFCcnf988ghdvhAH233WBcDJ3iEWVhVatamaMlSJb/UG+MnTR8lzCBZWmaFGdyKny17DC8DtzKOi0EWnL4g3EKa8YLiDpAR73QiiSZXaUGJdCSQ1G7LFG6Cu1IMrb/wy5OwFFew0nbQu8zdRo3O8NJrZjbr6nOrCiJrJpTPJ8Rr7Z3mk0080bnD/rlOTOSwe3ttOJCbzpkaaAdYxELIcr+pi95hzvHad7OOz97xKpy/IX527gM9u9nDe4qqk15SZzkZqSQk1WwxGL4g5EgPBCC19AZp6hoiMQZy8dKyHVm+AD1+yCLfTQfvAyMJLnejtQqnY7eTd5y/gL6+1cXKUNjZ2Ht7XTrc/xHvOH734apFbOj3jvUh74ahsQn2gLbk21ZFOH//73HHWNEghuf1EL/5QlG5/mAVVhTRWKeE13DF9dF87b/z+M7ze7uP/3bzeqkCvHC9gmOMFUlh1DIyQ42WKG1VKIh01JW6cDsEpb4C+wTCn+oNUFLo42uXHH4rS2hegYYIlUTYtrKDVG6CtP0C3P0Sx2zlsIsBMRAsvjWYEQjrUeFpgF14j5VJlQjlmD756alLbX/3x1VM0VhXicTloyyBqIrE4PYMhy2WQwit7xyseN/inP+6jtsTN/R+/iH+6bjVrqoefnJSz0Z9Svb7HJvJGq8s0EofapaCIxo2M7kw67t3ZQonHyTWr51Bf5hnV8Wr1DlFW4KLYnezkfOiiReQ5BD977ljW6/71i00sqCzk0mU1o75W5UqNZ/JG72DYCsXZXVXDMPjaH/dTmJ/H7e/bjNvpYPvxXktkNVYV0VgtBWZqSYlgJMYn7trF/MpC/vKpS3jrhkT+lNspKDQr3acm10Oier03ELEEuZ05ZR5KPE5WzMnc9zDPIZhT5pH9JM1te9vZ8zAMeK2ln1ZvwMrvGi8qnLzjRB/d/vCsyO8CLbw0mhEJRvWsxtOBrgk6Xur9rd4Ar5z0TsqYOgeCvHish+vXz2VueQGnMoQau83ZmIlQoxvvUCRr1+h3O5rZ09LPF9+0kpI07oZCnWBTQ412kTcR4XWgPeHkHMmyaXUwEuOhve28Zd1cPK485pR5aB8luT6Tk1JX6uHGjQ38bkczvvDo4vlg+wDbT/TynvMXJPUJzMREGmW/dEy6XSUeJwdtjte24708d6SbT1+9nPqyAjYuKDeFlxRZCyoLKfG4qC7OTyrVAHCsa5BwNM7HLluStluDyoUq9Qyvo15b4uFkj3x/ulCjx5XHC7ddwdvPHrkbSUN5Aae8QSvM+FfmZI5dzX209QdpmKDwOqu+lAJXHjub+mTx1FmQ3wVaeGk0I6JnNY7Mp3+7m0f3tU/3MEalyxe0HJBxCS9/iMaqQtxOBw++Ojnhxr+81oZhwHXr5zK3rCBj/pSqWm+FGkvkibAni3Bj/1CEbz/yOuc0VnDDhpFnjKnk+tRQoxJeFYWuCYUaD7YNWC7L0a7shNfuZi+BSMyqNVZfVjB6qHEEJ+Wjly4mGInzxMnRXc9H93UgBLx9U3btZiby/XrhaDdF+Xm8Zd1cDrb7LFf1ucPd5DkE7zBb3py7qIp9p/qtPDCVs7WwqmjYzMbDnVLALa9L70qpXKhMjlff0PA+jXZKPK5RBaksoiodrzmlHpbWljCvooDH9ncQixtJLZfGgyvPwfr5Zexo6qXbr4WXRnNaoGc1ZiYSi/OHXa1sPTSxfqmTRd9gmH9/6GDakgGdAyHmV8p2KeOZ1djlC9FYXcQVK2v50562rIp42onHDcLROOFonLb+AA/sbuVXLzWxck4Jy+pKmFvuyShqVNV6e6gRsqte//tXWugdDPNP161GiJFPkoX5ebjyxLDq9Wq22Pr55RNyvA62+1jTUMacUg9HbY7Xc4e7uW9XS9r3bD/eixBwTqNsz1NX6qGjP5SxnphhGLT0BTI6KUtrS7hyZS1PNY9eSPfVZi9LaoqpzLKgZ7HN8YrE4nznkddHnYGpeOFID+ctrmJtQxn+UJQWM09t+/Fe1jSUWW7a+YsqiRtw/65TVBXlWw5mYzrh1eEnzyGsUGQqKiyXWk4CEjlckNyncazMLZdCeU+L16r4v35eObtM13iijhfA5oWVHGjzccobsC5KZjpaeGk0IxDUsxozomZX5arMwlj5wROH+fHTR3npeM+w57r8IWpL3JR4nKM6Eo/v7+CSbz+ZJOC6fPL9162fS7c/ZIWGsiEUjXHRt55k+T8+xPJ/fIgL/u1JPnX3bjoHQtx6xTJAOjld/hDh6PDwYacpvOzJ9WqbRuNEzyClHidrzBmBIyGEoKzANSzUqNazbl453qFI1hchH/zFdv79Idl/Lx43eL3dx6r6UpbWFnPE5nj9xyMH+fqD+9Pmzm073sOKuhIrDFpf5iEci9M7lP475x2KMBSOjZi0/ca19fSHDA7YcqnuePEEV/2/py1BbRgGr7b0s27e6PtNodru+ENRXj7ey389dSSrGZxt/QGOdQ9y4ZIqVtZLd+pA2wDBSIzdzV7OW5ToCblxQQWuPEGrN8CCqoSgSldS4nCnj4VVhRlrjyVCjemT6xXpcryyZW55AbG4wdGuQVaZs0zt+3SiyfUga7TF4gaD4Zh2vDSa0wFVTiIci6c9KZ7J9JiCq3cGCK9Wb4DfbJOlAtLVmuocsAuvkR2vZw930dwb4KTZCy8eN+j2h6gpcXPFylqK8vPGFG5s6hmirT/IWzfM5XPXrOBr163iT7dezO6vXs2b19UD8gRkGAl3q2MgyN/csYOXT/TSMRDCIaDKPKnUKMcriyKqLX2BUcsg2CkrcFn9+xRqttiSGpknNFoBU5D77PmjPdy1/SThaJxWbwB/KMrKOSUsrS3maKcfwzDoD0R4rbWfvqFI0gQIkKUcdjb1cb5t9qWq+aScJMMwkr5/iWromU/oly6rBuCZQ93WY3dtb+ZIp98K4bX1B+n2h1g/r3zUbVUU5SeEl5qh+Mi+9lEnY7xovvaCJVWsMMOCB9t97G72Eo7Fk4RXQX4e68wxLbT1RkxXUuJwh5/ltZmT3y3hlcbRUiIfSJvjlS12R0s5Xuts+3SiyfUgS0ooM1cLL43mNCAUieNxyZ+JDjcm0zcG4fX8ke4xuURj5T+fOAyA0yGsMI3CLpxK3K5RHa+DZhJ4q1eexLyBCNG4QU2xG48rj/MWV1mhkmw4Zro7f33xIj5++VI+cNEi1jSUWa16AOrNYpMq3PjovnYe2dfBO3/yIvftaqWmxG3101PhlGxKSrT0DY3p5FZemI83ZVZjly9EdXG+VYk8XdmL+3e1JuVtdfiChKNx+gMRnj3cxQFzVtvK+lKW1BYzGI7R1h9k27EeVMRPvUax91Q/wUicc23CQxXsVDMbH9nXznn/+rgluNRnP1LuUG2ph3nFgmfN+mnNvUPWrLvnj8jv6B6zyOdYHC9rVmMwygtHuxECjncPWnXFMvH8kR4qCl2cNaeUIreThVWFHGwfYNsxGWbd3FiZ9Hq1P+wJ86qkhEqwD0VjnOgZZFld5obTqqfhaI5XphyvbFBFVCHRZ3TtvDKEkKHOySj9UFbgsgSmFl4azSwnFjcIx+LWjzmXCfZfuHcPtz+T/TT3mYByvPoyhH3s/OP9e/nGn/fnZBwnuge5Z2cL7z5vAfMqCqwq3Yq+oTDRuJFVqNEwDKsti3LOOs0G1ap58Io5JRzt8mftgB41K9Ivrsl8ElSiRgmKva0DVBS6uGFDA61moUlFYb6Twvy8YW2DBoIRXrfNHDQMg+besTte6WY1Vhe7rbBQqqM4GIryD7/bnfT9PWErbfDHV09xoM2HELK/3lJzPxzt8vPC0R7yTQF6sD25ftW2Y7JFkF14JRwvOYbnjnQTiRlWEc1sHC+ANdVOdpzoYygc5RFzckh1cT4vHJUu2Kst/TgdIqkI62ioPKwOX5BXW/p5x6Z5CCFrtWWifyjC04e6OH9xlZWoftacUg62+dh+ooez5pQOa9lzniW8Ep/r4poiXHmC3c1eQAq+uAFLazN/585prGBNQykL0+SA1dodrwmGGkHmv6mq+dI9LaZhDN/L0dhklpWo0TleGs3sJmSWkqiaAuH19KGunDpCuaDXEl6REROV+wbDHO8e5GjnYE6aLP/gicO48gR/d/kS5lcWDnO8VAirttRDicc1Yh2vLn/Ims2llqMEjpoFtnJOCdG4MWpjYsXRLj91pe5hdaXszC1TbpJc52ut/axpKOP/vXMDt79v87DmwenaBn39wf287X+etyqn9w6GCURiY3O8ClxpZjWGqS52U2u6bqmTAA60DWAYyTMVT/bKfXPJsmoe29/BruY+GquKKMx3WmLgSKefF4/2cO6iShrKCziY4nhtP97DkpqiJBejukgW5VQCVTmPe0zB0dI3RGF+3qhiYW11HuFYnG3Henl4bztn1ZfylnVzeflEL+FonD0tXlbWl4zJkXHlOch3OnjqYCexuMENGxo4e0FFRuEVjcX5xF2v0B8I8+FLFlmPr6wv4XjPIDub+pJEp+LipdX803Wrkhp2F7mdnLuokicPdgJwqEN+FplmNAKsnFPKn269JK3j5XHlUeJxkp/noGACrlRhvlO6efUlSTMgv3HDGr78prPGvdxUtiyvIT/PwfzKyRNzuUQLL40mA2pGY405+yeXocb+QGTGNW8eDeV4xeLGiGJGXYUHIjHaRikFMFaae4d4YHcr7z1/IbUlHuZVFNCa4nh12oRT6SiO16H2hHho8aYXXupk9nqHj2w41jXI4urMzgPI3J3yQhdt/QGCkRiHOnxWi5yrV9VZs/oUqUVU/aEof97TxmA4RnNfcthtLCejskIX/ekcr5J8nHkO5pQOn335WqvMi7LX5mrqGcLpEHz00sUMhWNsfb2LlXNUOCifUo+Tl4718HqHjwuXVrFyTkmS4xWLG+w40ce5i5Kr6zscgrpSD+39QYbCUes9qg2NquE12gzOZRUOPC4Hv3+lhZ0n+7h29RwuWFJFMBLnlZN97GnpT8pFypYSt5NDHX7ynQ7OXljBtavnsL9tgObe4QVjv/HnAzx7uJtv3LCGTQsTn+/KOaUYhjz+nJdGeDnzHHzwokUU5icL+StW1nG408/JniGOdPhwCFhUPbx+V7bUlXooK3SNui9H468vXsR7L2hMeuz8xVVpReV4ecPqObz8j1clhUhnMlp4aTQZUDMaq4py63iFo3ECkdisa0vUOxiy3c8cbrQ3Js62cGa2/Py54+Q5BH998WJAtonp9oeHzUgEskquV2Jq5ZwSK6SWKrwW1xSR5xBWJfaRMAyDY11+FteMfgKcW1ZAmzfIoQ4f0bgx4kzE6uJ8Wr0BK3H7L3varIkgh81taOnLLuxmp6zAhS8UtVyzSCyOdyhiuU5zyz2WIFXsbZVOVd9QhB5TDDb1DDG/spALl1RbpQlWzpFhOyEES2uLefyAdGcuXFLNyvoSjnT6LZf5QNsAvlCU8xcPPzmr6vWvtfQTixssrCpk7yl5v9WbuZSEnfw8wXmLqvjTHllL7Zo1dTLcJ2RTbF8wyvox5HcpVLhx04IKPK48rlktXalHbLXumnuH+OIf9vDLF07w1xcv4p3nJDffPqs+4VKNRZyoWmdPHuzgUIefxqqiCeVQ1Za4h4U5x8MnrljG9evnTng5ozEZY50qtPDSaDKgTmRVluOVm5ISyi0aT32p6aRvMDHeEYVXs9fq6TaZwqt3MMzdL5/khg0NVu6PEhkqMR7sOVpuSjwu/KFoxplmh9p9VBXls35eomZVly9EgSuPIrP4p9uZx6Lqoqwcr57BMAPBKEtGyO9SzC330OoNWA7S2hGE1xUra2nqGeKP5uzKe3e2WNuukrlbTOdvLLWSVM0m1ZtRFWlVwktWIk8VXv3WvlGfb1PvIAvMumlq5uZKm6BYWltMLG5Q4nayZm4pK+eUEo0bHO2UIUoVdk8nPOaUeWgfCLLLdFLfe/5ChsIxjnT6pfDKskTBpctlG6DGqkJZsqLAxZqGMv60pw1gXI6XCidftFQ6dQuqCjmrvpT/fe44n7p7Fx/+v5fZ8p2t/H5nKx+4sJEvvnHlsGXMryikMD+PpbXFVppDNjRWF7G4uognX+/icKdvxPyubPjE5Uv57BtWTGgZmvRo4aXRZMByvMyDX65CjSqnZvaFGkNWJfJMwiseN9jd7OXylTWUF7omVXjd8WITwUicj1662HpMiY9mW55X54Ash1CY76TE4yRuwGCaIqsABzt8rJhTQkNFAV2+EMFITNYAK3UnhVxW1JVYSfgjccxKrM/C8SovoK0/yN7WAcoKXCM6VW/fNJ+1DWX8618OsO9UP9tP9PLu8xYwt8xjOV7NfbJnYbocnkyoJspec8KECmcmHK8C2vuDVr2rQDjG4U4f15j5Rke6ZJmIpp4hK/n7Pecv5ILFVUlhMyUKzltchTPPYbk8qk/hg6+eYnldMfVlw/eBdLwCvNLUx8KqQraskE7Pi0e78Q5Fshaaly2XZSWuWT3H+mwvWFJFLG7gcTlYNg7hooTXBUuqrcc+fPEi3K48Xm32cqxrkPddsJBnPn85X7t+ddLMVoXDIXjv+Qt5/wWjN+ZO5YqVtbx0tIcTPUMjzmjMhguXViflkWkmDy28NJoMqBwvVeE5V6FGVTdpJCdmJtI7GLacnEzC61i3H18wysYFFSytKc66VcxoBMIx/u/FE1x1Vi3LbAnEagZfiy2nRhVPBaxK3+ncxXjc4HCHj+V1JZZrcsoboMsXsmpnKVbMKeFk71BSwcp0qO3NxvGqLyugPxBh2/Ee1jSUjphbk+cQ/PNbV9MxEOKDv3gZh4C3bZzHsroSK7Fa1vAaW50kFa5R1etV8VQ1W2xueQHRuGG5iAfaB4gb8IZVdRS48jjS6advSOYrqnIHS2qKueuj51uiDhLC68Il0hlqrCoi3+ngYLuPV5u9vNrSzy3npRcedaUegpE4Lx7tYeP8chZXF1HidvIXM4k921mcS2tL+J9bzuZvtyyxHrvQFExr5palFUWjUexxUpSfl1SG4qZN83jqs1vY+rnLefKzW/in61ZbDm0mvvims4blRWXDFWfVEo7FicWNERPrNdOLFl4aTQZCKTleuXa8YnHDCm/OBnoHw9YJNFMlcdVQ+uwF5SypKU5qFTMR/rBLtsL56KVLkh6vKXaT73QkzWzsGghZ+Vmq1lI6d7HVG2AoHGPFnBJbyNIUXiXJwmt5XQmGIYtUjsSxLploPTeL8Ndcs+bRsa5B1swdPb/o7AUVvH3TPDp9IS5ZVsOcMg/LaqW4jcWN8QkvczagSrBXRVqtUGNFQpAC7FNh0XnlLKkt4kinnyazdc3CEZL6z19cxS3nLeD6DTL3x5nnYHldMQfaBvj1S00U5udx49npe0sqF8wXkoLe4RCsaSjj5ROy/MRYqqG/aW19kiA8p7GCAleeVZ5grLz73AV86c1n4RqHaJsMzmmspMR03SYaatTkDi28NJoMBM1E32KPk3ynA/8o7sZ4GbCJgNkSbozHDfqGIswt91DgysvYNmjXSS8lHieLq4tZWltMz2B4wi2GDMPgjhebWNNQyjkpJ0iHQzCvvCBJeHX6gmmElxQW9+9q5a3//Tw+Ww2s5XUllsBo7QvQ5R8uvFbMyW5mo5zRWGQVPx0JuzjLpsUPwBeuXclZ9aVWOYLldSWEonFO9g7R0jdk1U7KltRG2d1pcrwgUUT1tdZ+KgpdzC3zsLSmmGNdg1b1dHudqVQK851888a1SaUiVs4pZU9LP3989RQ3bGzIGCK1u0UbF5QDsG5+Gcosnkg19MJ8Jw/eerHVymmsXLWqLqNTNxW48hxcuqIGh8jOZdVMDzkVXkKIciHEvUKIg0KIA0KIC2zPfUYIYQghqkdahkYzXahQo8floNjtzLnjBckhsHjcyEndq8lgIBghFjeoLHJTWZRvlZZIZdfJPjbML8fhEIn6TRMMN+5o6uNgu4/3nr8wbTiuoaLASiwH1WdRnqxVqFGJ3eeOdPNqs5d/fnC/JaKW1xUzp9RDnkNwrHsQ71BkWKhxQWUhbqdj1JmNx7oHs8rvgvEJr5oSNw996hIuWSYTxZeaeT3bjvUQjMTHH2q05XgV5udZs/XmVRSQn+fgL3vaMAyDva0DrGkos2YqtnoDHGgfQIixlbEAOZO0PxAhFI3znhHEi6pen+90WDMlVWuf/DzHsM9qrCytLR6x5tpM5zNXL+f779o4KVXhNbkh147XD4CHDcNYCawHDgAIIeYDbwBO5nj9Gs24Ucn1HmceRe683M1qTBJeCXH36d/t5pN378rJOl861sMx7/i3RwmtqqJ8Kovy07pYg6Eohzp8bFwgXSl74cyJcMeLTZR4nFy/Pn0oal5FoojqYCjKYDhmVeK2t3UBOfNPCDkr8K7tJ2koL6DE47JqVqkaZKmOV55DsKyueETHK2w6T6PV8FLUlbhxCFkLaqQw3UiohHBVSHMsVetheI6XqlqvKMx38g9XL+fhfe389uVmDnX4LJGoPt+nDnYyp9Qz5hO/qhK/eWGF1dcvHTXmflrbUEa+U57CVE5VfbknqVDnmcjimuIpKd+gGT85E15CiDLgUuDnAIZhhA3D8JpPfw/4PDAzL+c1GhLlJDyuPIryRy68OREyCa/XO/xWaYGReHhvG7felb1AC0VjfOzXO/nuzqDVlHmsqGT6yqJ8Kory0ybX7zrpJW4kwkEN5QW4nY4J5Xl1+UI8tLeNd2yaT0F++hP7vIoCegbDDIWjVkNn5YKk5ni19AV409p6Vs8tpaUvYIUQ1XhfMwtzpgovgBV1pSPObDzZO0gsbrCkNjvHy5nnoK7Uw6q5peMWDyUeF/VlHp47IlvfzKscm+PlzHNQ4nbaQo0ha3KJ4qOXLmbzwgr+8f69ROOGVfZChbYOdfhZMA7huKahjIbygqRk93S48hycv7iKN9pm3DWUF1BVlD+m/C6NZrrIpeO1COgCfiGE2CWE+JkQokgI8Vag1TCMV3O4bo1mwkxPqDGxDu9QmLb+4KgzHZ862MWDr56y6k6NxpMHOvEORRiKwOfu3TOumZSqvlNlUT5VRflpk+t//0oLxW4n55pV1x0OweKa4jGHGg91+PjqA3u5Z0czP3n6KJGYwS3nL8j4+nm2/Kx7drTgEIl6UPZZjbG4QVt/kMXVRXz/nRtwOx1JtbPmVRRY4jtdRewVc4rpGAhZYblUrB6NWTpeAF9601n8w9XLs359OpbVlTBklssYjxApLXDZkuvDwxoP5zkE3715veU2qYkAC6sSuWyNVWOvmF5W4OL5267gyrPqRn3tbz5yPh++JFFGRAjB19+6hk9csXTM69VopppcBrKdwNnArYZhbBNC/AD4GtIFe8NobxZCfBT4KEBdXR1bt27NySD9fn/Olj3TmUnb7g3F6QkYLCnPPjzR7IvjyYOawvFdP4y2/QeOyhPq9hefJzQYoids5GR/HTkZxJMHwRjseHUvRb2vA9DjCxKOw4OPbqXUndkBOdgkXas7/vI8588d/Sf9451Byt2Cqxri3Huoi3+643GuWDC2qs8vNcsT8+t7djLYG6GrP5q0bwbCBg/uHuKy+U5efvE56/FSI8jek74x7cefvRbiudaEIF1d5aB53w6aM7y+ywyh3vvENv5vX4hz6vI4umc7R5GJ+QLY+/pRPJVhYnGBv/MkrQfa+LeL3RQ7Wtm6VRYljQwkBNWRvTvpOZL8PQt2yTH94k/PsLF2+H5//Jh8/6nXd9F3NDsHqwQI9sHWCSRhFITkTMQiF+x86fm0rxnpu++Mhzja0s7WrVs51TtIfX4g7Wvff5aTbW1wdM82jpm5djUF0D4Isf52tm7tHf9GjIMiINwLWzN9MWzMpGPfdKC3f3q3P5fCqwVoMQxjm/n/vUjhtQh41UyKnQe8IoQ41zCMpE6ihmH8FPgpwObNm40tW7bkZJBbt24lV8ue6cykbf/aH/fx+10t7PnaFVn3BnvjD55lcXUR//2ms8e1ztG2/5Xw64gjR7j6ii080LGLA20DOdlftx95iQWEONThp6FxCVsuWUwwEiP88MMANK7eOGIV7e/tfQ7oZ6hoDlu2rBlxXZ2+IHsffZKPXrqYc9xtdOQV8ttDvbzSLx2da1fP4RNZzOja99QR2Pc6b77qMtqePcajTYe44OJLcDulcP7R1qNEjYPcdtOFSXW2Xo0eZvsThzjvwksyhgrtxOMGn33ucd68rp5PXbmMV5r6OH9xFY0j9KBb5QvyjZee4JHWPIIx+Mo7LkhKVi95+hEq6+YyFO0Aglx1/kYuWjp8jk974UkePPoaAG+5esuwEgHnR2Lce/wZ/tzi4BM3XTLs+Qc6dlNX2s0br7p81O2cTDqKTvJI02ssri1jy5aL075mpO/+vCMvcaJ7iMqlG/A/8jzrli9iy5bhLly6d687uYP2/R1sOWcNW9bN3DyjmXTsmw709k/v9ucs1GgKqWYhhOo5cCXwimEYtYZhNBqG0YgUZ2enii7NmUenL4gvFLX64mVD32CYnsHsXz9WgtE4bqcDIQTF+ZMXavzjq6eSEswHAlGrNpGabddnC1+d8o6ch6Wm/O840Zf2+Yf3tvPCUZnzc/+uVmJxg7dvmodDCL7zjvW8aW09dSUeunwh7tvVmnYZhmFwx0tNtPfLsfT4wxTl5+Fx5VFp1jlTLYRicYM7tzVx/uLKJNEFMgHbMMi6kOqe1n66/WGuPquO5XUlvOvcBSOKLpD5XG6ngxM9Q1y8tHrYDMESjwtfMEp3QIaSM838UyUlKovy09Zl8rjy+OpbVnGk08//vXBi2POvtnhZ21CexVZOLktr5T4fb1mF95y3kIFAhOv/63kMI9EkPrt1y7DqeEKNGs2ZQq5nNd4K3CmE2ANsAP41x+vTzFJUcnaTreL4aPhD0ZzWvQpGYtbMrCK3c1JmNcbiBp/53W7+9/nj1mP9gQgVhS6K3YkGzvY+iKm98ewYhkGXP4TTITjYPpC2uv7n7nmVd9++jW/8aT/37GixipmCrAL+vXdu4OcfOIerV9VlrEDf0hfgK/fv5RfmuHsHQ1SaJ+TKIhmmVCL46UOdtPQFeO/5jcOWo2afbX29M+M22XnyQAcOAZeZffWyQQhhiSZ7OyFFicfJQDBKd8BACNK2pYFEftRI5QmuPKuWLStq+P7jh61q7iDLbRzrGhxXo+WJolrFjFd4vXFtPc/ddgWffcNyltcVs2lh9o2aL19Zy4b55bqGlEYzAjkVXoZh7DYMY7NhGOsMw7jBMIy+lOcbDcPozuUYNLMDS3j1ZCe84nEDfyhqNZi2Px6bpNpXgXAMjxk6K3bnMRieeEufjoEgkZhhOUcghVdpgYsST2LmpD1hW83MS4cvFCUcjXPBkiriBuw2K8UrBoIRfKEoi2uK+Nlzxznc6eftm+anXVZlYT7eQCTt/jvQJnvo7WiSP+GewbDldKU6Xr9+6SQ1JW7esHp4kvT8ykIuXlrNb7adJBqLZ9wuxRMHO9m0sIKKouxdF4BV9aWsn1/OJcuGhxBLPS58wQjdAYP6Uo+VJJ6KqquVbkajQgjBP123mnA0zn88/Lr1+F5zNuS6+eVjGvdkUOpx8YN3beB942g5oygrcPGJK5bx6D9cNmJph1TOaazk/o9flFUYWaM5U9GV6zUzgl7zpK3ajYzGoFlFPtXx+ty9e/j4na9MypiC0Tgel/yJFLmdGAbWbLHxoupLKeEVjxv4ghHKTOHlt0KNcn84xMihRhWafcOqOoSAHU3JCc3KLfv01cu5/X2buWHDXKtNSyoVRfkYRvIsS8VBs1Doay39hKIx+obCVBUNd7wC4RjPHe7mhg1zM7ZNec/5CznVH7RqTWWivT/IvlMDXL6ydsTXpeO7N6/n7o+cnzZfUAnc7kB8xDpXHlceDeUFViufTCyqLuLtm+fx4J5TVu23V5XwyrIQ6mTz1g0NYy5gqtFopgYtvDTTjmEYVk5Tto6XCqn5gsku1JFOH6+2eCdlXKmhRph4v0ZVUV3Vz/KHo8QNTOHlwhcyQ43m/lhSU8ypERwv1UuvsbqIFXUl7GxKzvNqM0VbfVkBV6+q4/vv2pixKnelKaR60+TNHWyXjlc4Fmdvaz+9/jAVZo87dds3GGZHUy/hWDxtsrriqrNqmVPq4Y6XmjK+BhJFQK9cOXp5gVTczryMrkuJx4kvJB2v0cJxP//AZj77hhUjvgak8A1G4rx4rAeAPS1eFlQWjtmp02g0pz9aeGksorE433r4IN3+3CWsp2MgELXCW9k6XsoZSm0s3R+I0D4QJBwdPYw1GnbhpcRKuhyqTBzp9PFvfznAMVsieXOvFFE9g2FC0ZhVPLXUo3K8kkONq+aWjpjjZe+lt7mxgl0nvUmhQlXbazTXBuzCK43j1eaz+iLuONFHz2CYKjPHq7wwHyGgdyjCC0d7cDoE5zRmzgty5jl493kLePZwN8e7h3/ewUiMYCTGEwc6aCgvYHnd5OYLlXhceAcj9AZHF14r55RSWzr6vjt/cRUFrjyePCDF4p6WfiufTaPRaOxo4aWx2N82wI+2HuV3O7IohDOJqOKbxW5n1sn1PpsAsocb+wMRDGPkhPRsCUWSQ42QnfBq6w/wd3fu5OrvPcNPnjnGb237095DsHMgZIX1UnO8+oYiFObnsbCqiE5fiEiGfCglkquL3WxeWIk/FLXcKTWWPIdIWwA0lUyOVyAc43jPIBcvraGxqpBnD3cTisat1+c5BOUFLnoHQ7xwtIeNC8qt/ZWJd50zH6dDcGeK63X7M8dY+ZWHWfmVh3niYCdXrKzNurxItkjHK4rB2FvqZMLjyuPiZdU8ebCTLl+IVm/A6h+o0Wg0drTw0liok/iLR3umdL3qRL9+fhneoYhVNXsk/EG78JKvNwzDKsegcqky8dzhbn61f2RnLxi1hxrlrRJe6fKgFN9/7DCPH+jk41uWsri6iCMdCcerpS+A0hHtA0Gb8HJaZQ5AhhorCvNpKPdgGCQl49vp9odwCCmaNi2UjpQ93HjKG7QaPo9GJsfrUIcPw4CV9SVsWljJS2Y4rdIWRqssyqepZ4jXWrxcsGT0vve1pR4uX1nLQ3uTK8m8cLSbuWUevnDtSr70ppU5qUSuqtfD+Gf+pePKlbW0egPcs1MKbe14aTSadGjhdZpxyhvgN9vGV/a62yedp5dP9BKK5qYhdDrUiX7jfCkcmnpHDzfanaf+gLw/GI5ZYTa7s5SOh/e18eTJKEPhzA5WMGKf1ahyvGLsbOrj7H95jJ8/dzzt+452+dk4v5zPXrOC1Q1lHOpM9PNr8Q6xwqxt1d4fZMAce5nleMl90T8UobzQZZU6aDOF166TfTyyLyFWuv0hKovc5DkE8yoKqCh0WTMQQX4fsgkzgi1XK6UFjnLQzppTyubGCqLmPq5KEV4vHu0hbsCFS6qyWt+G+eW0egPWNoPs87e5sZK/3bKEj166hLoswnxjRfVrhMlzvABrEsDtzxzDIRhWP0yj0WhAC6/Tjt/vbOFL972Wdd8+O12m4xWMxNmVUpYgl/SZpSRUM+VsEuzTOV52F2o0x0vNBmwd4XWBSGxYqHEwFOWPu2UR0m/+eX/aelQnegatApLLaotp6QswFI4SjcVp8wat/KeOgWBSjleJ20koGiccjVuOlyppoEKn//Kn/XzpD6/ZtiNsNTEWQrCsroRDNoetrT+YsU5VKrIZeJ7Vh1FxoM1HUX4e8yoK2Gy6akBS4nhFYT7RuIHH5bA+x9FYbgrQw2Yx2cFQlFZvYNJzulJRwksA9VmK0myoK/WwpqGUvqEIy2pLRg23ajSaMxMtvE4zekwRs//UwCivHE63P0S+04FDwAtTGG5UY95g1jw6mUWeV7ocL3uIcjTHSwmvlhEEajASH5Zc7wtFeWRfB5csq2bFnFJu/c2upCr0vmCEbn/Yqq6+vM6s1N45SPtAkGjcYNXcUjwuh3S8TNFYVuiyBIEvGMFrOl7KrTrVH6DHH2JXs5eewbAlVrv9oaQ6U8tqiznc4cMwDOJxg7b+gCXesqGiKD+t47ViTgkOh2BJTTFlBTJUZ3e8VKL9OY2VVtug0VDO3yGzVIWqZq8qr+eKUjPUWOkRGUtejJcrzBmYOsyo0WgyoYXXaYY6aY5PeIWZW+ZhbUMZLx6durq2fUNhPC4HVcVuakrcnEgz0y2VZMcrOe/K6RCjOl5qNuBIr0tXTuLFo920DwS5cWMDt79vE26Xgy/+YY/1HuXWNVbJEJYSEYc7fda65lcUUl9WQJuZ4+UQUJzvtHKP/KGo5XgV5jspK3Bxyhtg6+tdqMoZSqR0+0NU2yqrL68rYSAYpdMXonswRCRmZB1qBCmmemzV6w3D4ECbj5X1soimwyGsXLLKFMcL4IIsw4wg86sK8/OsGmHKqVs2RY5XdcHkJu2DLJUBsHFBxSiv1Gg0ZypaeM0ADMPg3p0tvOunL2aVWD4SqgL8vlP9Y35vt0+exC9YUs2uk95J6004Gj3+MJXmibuxqjCrmY3+UMRKUk8NNS6rK5mUUGPI5ngVmreP7+/E6RBcubKOeRWF3HT2PF5t7rdmHZ4wy2Eox6uxqhBXnuBwp98a07yKAupK3XT0S+FV4nHhcAiKTUHQH4jQH5COF8gK6m1eWXC0wBzHkU4/hmGYwishgJaZvfIOd/itwqtzsww1gul42YSXmgBw1pyEC/WmtfWsaShNqgemRNhFWSTWKxwOFRqVwutwp4/8PAcLc1z4Uwnc6oLJP/ytm1fOnR8+j5s2NUz6sjUazemBFl7TzEAwwqfu3s1n73mVl4718nqHb/Q3jYASXvvbxhdqrC52c9HSKqJxg5dP9I7+pkmgbyhs9f1bUFmUVS0vfyhKVZEbh0g4Xipst6q+lA5fMOMEgcFQ1Kr9lSkXLhY3CMcS5SQcDkGx20k4JtvzlJmiaNXcUsKxuOVAKbduoel4OfMcLK6W4b+WviHZG7Dcw5xSD+1mjpcK3Skn5pQ3QNyQ9bEA5pZ5aOod4plDXVy3vh6308GRTj+D4RjBSDzJ8VJNqQ91+Ggzt20seUyVRflJ/RoPtsnvo3K8AN6+aR5/uvWSpDIPb1k3ly9cu5K1Y0woX1FXnBBeHX4W1xThnOTwXyrFOXS8AC5aWp11uFWj0Zx5aOE1zfztr3fy59faeKvZxsUXnJjjpdyKlr7AiCUP0tHtD1Fdks/mhZW48sSUlZXoGUxUQV9YVUjHQMhqvZIJXzBKqUeVYJDbqRLVV88tNWt5pS/BoNwugNYMuWBKtCnHCxIlJa5ZPcd6bJUpSFRo93j3EHWlbgrzE27Q0rpiy/GqK/HgduZRV+ahcyCENxChtEC+VuUeqRy3CpvjdaTTjy8U5aqz6lhcU8yRLr+1HXbhVV2cT3mhi8OdfktUNowhx6uyMFl4HTBnNK6YM3Le1ZwyD3+7ZQmOLMpW2FleV0K3P0y3P8ThTp8lHHNJbYmbN6yqY32tFkcajWbq0cJrggQjMf7uzp1ZV1y3YxgGu096ueW8BXzyymXA8N6DY11ez2CYleZJ8sAYXK9ILE7fUITqYjcF+XlsXFAxZQn2fYOJvn/KKWrqGeKOF0/wN3fs4PH9HcOaU/tDUYo9Tko8Tqt2V39Ahh9X1svtz5Rgr+qVlbtFRscrGJGhQ4+tgXKR24kQsj2MYnFNMR6Xg32m8GqyzWhULK8t4WTvEIc7/VbdqPpSD+FYnKaeoWGOl6pur8SocqzynQ4uWlrN0tpijnT6E8VTbcn1Qggrwb6tP0iBK89afjZUFOUTiMQImD0pD7X7aCgvsEThZKME3avNXpp7A1aoNJe48hz89H2bWVymhZdGo5l6tPCaIMe7B/nLa+3jcocGAlEGwzEWVBYmzWgbL4FIjFA00Sdv3xgS7JXLodyTVfWlWSW5TwZ9g2GrNMFCU7T87a938pUH9vHCkR4+/KsdvOmHz1khKZDJ9cXuZMerPxCh1ONigZkjlCnPSzlFS8oddAyE0oYkVSjS7njVFLs5Z2FlUguZPIdgxZxSy/E6kUZ4LTNnNu5p8VrCa06Zx3q9EkYqZ0o5XirHSzlW5y+uosjtZGlNMa3egCUs7Tlecn0lHO70WzW8xlL5XQlg1U3gaNcgi2uKRnrLhFAzG//8WhtAzktJaDQazXSjhdcEUQ7VeJyqFq88cc61OQoDE3C8VP2lFXUl1JS4xzSzMTVsVVPixheKWs5HrghFY/hC0aTkepCi6Z+uW8UrX72a/3fzepp6BvlfW8FSf0gJr2THq6zAxZxSjzmzMb3jpeqVLS6TX/+2NCHJYBrh9YN3beS/bzl72GtX1Zeyv21gWCkJhXJxDAPmm6JQFQY1jESIUSV9N1uhRjPHyxReV5oFOpfWSiG3/bisUF9jCzWq9fUHIuxp6R9TKQlI1ObqGwxjGAbHuvwsqcmdGKopcVNR6OKxfR1A7ktJaDQazXSjhdcEUXlF43GqVA5SQ3kBbqcDV56YUKhRlZKoKMq3xEC2qLBVTYk88aqTea4bZnvNWZyVtobLP/yrjTzwiYv44EWLcOU5eNvZ81hYVZQ0Fl9QhhpLbf0NVaK6M89Bfbkno+PV7ZNtdhpLpahKF25MJ7zmlHmSamYpVs0tpT8QsUKzSjwqGquLcJq5T6mOF2A5XvlOB26nwxq3El5nL6jgn69fzTs2zwOk8ALYdqwHIZLLOgAsM8VLqzcwphmNkHC8egbDdPpCDIZjLMmh4yWEYHldCb5QFFeeGLbvNBqN5nRDC68J4guZid3jEEyqGvnc8gKEEElhs/GgwoWVRS5Wzy3lcIcv69Y/qq6V3fGChDuUK5RLpxwvgOvXz+Us2yw6Oa58umwV1f2hKCVpQo1KxMwrL8wcavSHqCzKp6ZQiqF0JSWsHC/X6D8RlWD/kBkuS3W8XHkOFpmPqRY1NcVyRibIBtmKEo+cOekQiZyvPIfg/Rc2Wgn7jdWFOAQc6x6ksjB/2CxAe7hurJXZ7Y7XUbMw7OIcOl6QyPNaXF2c8xmNGo1GM93oo9wEUb32BsbleAXIdzosl6HE5t6Mh4TwcrNqbinRuMFhW/uYkbAStVOFl29yhZcvGOG23+/hnx/cByS7dCNRXeymxxyjYRhWcr3d8eq3zRCcV1FghexSkW123FR6BA6Rvnp9KI3jlYmz6ksQAh4/INsHLUzj2qj2OMrxcuY5rH2cLLzk/fLC/IwzBN3OPCuPrbp4uANXU+Km1BRt43W8egfDHDVz/HKZ4wWJfbNU53dpNJozAC28JohyW8aX4xWgobzAOsHamySPB0t4FeYnyhxkGW7s8oUocOVZFdqrJxhqTJ2FCPDKyT7e9MNnufvlZu586STBSMwac9Wowiufbn8IwzAIRePE4gbFbpfleBmGQX8gmnC8Kgrp9KUvS9FlttlxOgR1pZ60uWDBNOUkMlGY72RRdRH+UHRYKQnF+vlllLidSX0T55h5XmUpjhckEuszocKN1SXD95sK3wFjzvEq9bhwCPldOtblpzA/zxpnrlCO13Kd36XRaM4AdBfXCZJIrh+f42Vv51Lidk3Y8cpzCEoLZNJ5sdvJb7ad5LLlNVYydyZUDS+F6r03Hsdrd7OXd/7kRZbWFlsNobcd7+Vg+wBzywq49Yql/OeTR9h10msJr9Ecr6piN8FInKFwjCEz4b/YnYdDQNyAwXCMgUDEco+Us3TKGxgWKuv2hVhSXQRI4Zsu1BgIZx9qBBluPNY1fEaj4oMXLeKGDQ3k28pTyM+k33KnICG8KgpH3h9Laot5/EBnWscL5EzKHU19Yw41OhyCisJ8eofCtPQFWFxTNKZZkeNhzdwyLl9Rw9W2Mh0ajUZzuqIdrwkykMHx6h0ME4sPd33snEpJfp5oqFH19xNC4HAIvnnjGl5v93Ht95/hiQMdI743teefK89BRaFrXMJr18k+QtE4RflO7n75JL99uZmqonw+c/Vy/vKpS/jwJYsRArYf76V3MIwQUD5KrSm7A+c3WxkVexL9Dbt8IcKxuM3xkvv1Ww8f5Pc7W6ztMAzDcrwAGioKRk6uz7IC+aq50mHMJLxceY6kMhSQSLC3O16qpETFaI6XKSYzCa/zF1dRU+IeU/FURWVRPr1+6Xgtrs59+K8gP49ffPBcax9qNBrN6Yx2vCbIQJpyEsFIjEu//RS3vXEl7zl/Ydr3haNxOn0hGirswmviyfWVRYkT9ls3NLB6bhm33rWLj/xqB89+4YqMJ+JuX5gFKblJNSXutKFGwzD49UtNnL+4Km2l8ZO9QxTl5/HbvzmfaNxAwLCk6bPmlLLteA9LaoqtmYgjoWpVdftD5OdJMVTsduF0mK1/TNdKiZh188p509o5PHe4m0f2ddBYVcjWz13OQDBKOGq22YnLGaV/3tNGLG6QZ8upGkuoERIJ9qmJ9SOhhFemHK+RsEKNGYTXWzc08NYN4+sXWFGUT1t/gFZvgHdsmj+uZWg0Go0mPdrxmiCpfQIh4crsbOrL+L72/iCGkZyDMxnJ9amlBZbWFvONG9YQN+DgCPleqY4XyJN6OsfrJ88c4ysP7OOu7c1pl9XcO8T8ykKEELjyHGlF1XmLK3nlZB8dA8GkGY2ZSDheYWsmqarjBYkq9Up4FeTn8T+3bGL3V9/AZ65ezomeIdr6A7ayGQnHKxo36BhIruWlZjUWZCm8zl5Ywfp5ZVyyLPsm0RcuqebsBeVJYjgRahzZ8Vo5p5RNCys4d1Fl1uvLlqqifPa3DWAYuU+s12g0mjMNLbwmSKKOV9RKKFe1qUZq2ZOuj16px4k/HCU+SogyE+mEF2DVYVKNnFOJxuL0DoWpSamALh2vcNJjj+/v4FsPHwTAG0h+TtHUM2TNusvEeYsqCUbivHi0J+2YU0kKNZritMTjtNwiVToitbWNwyG4bEUNADub+oYVilXlHVq9AR7e287X/riPWNywQo3uLHO8Sj0uHvjExawZQ5PoDfPL+cPfXZTkqmXreBXk5/H7v72QTQsrsl5ftlQU5ROJye+gFl4ajUYzuehQ4wRRocFY3CAQiVGY77RKJBzt8hOOxpMSqhX2Gl6KEo8Lw4DBcNQ6AY+FTMKrvDCfyqJ8jnWlbwHUOxTGMJJ7/oGsNWV3vA53+PjU3btY21CGPxS1RKcdwzA42TvEFlPsZEIl3ftC0VET6yFRJLTHH7ZcqGK3k2hcOlPNKY6XnbPqSylw5bHjRB+bG6VQqSlx00ZC+H71gX2WUL7p7HmEIjGEAHeazy6XlLizS67PJXYHctEYQqcajUajGR3teE2QgWDUKoSpwoR9puMViRkc607vMinHq95WwTzRr3Hs4cZY3MAbiGQM2y2pKcoovLp9Uiimtp6pLnETiMQYNJPZ79x2kphh8NP3bqauxGM5e3a6fCFC0fiojldVsdtqpTNaKQmQVd3LClwZk+tbUnK87LjyHGyYX57keFmhRlN4HWgb4K/OlflMO5p6CUbjuJ2OnM/oSyXbUGMuUSK3obwgbWkMjUaj0YyfnAovIUS5EOJeIcRBIcQBIcQFQoh/EULsEULsFkI8KoSYm8sx5BpfMGKVarAqqA8lQnCZwo2nvAGqi91pw0wjCa9ILM53HnmdHSd6kx7vD0QwjMxlGRZXF2cUgVbx1DSOFyRKShzu9LGiroQ5ZR7KClz0p3G8VIPn+aMIL8DKT8rG8QJZ4qLHH7b2jz3HKzW5PpXNjRXsbxugqWeIPIewZlEW5Ofx729by28+ch7/9rZ11Jd52NHURyAcyzqxfjLJNtSYS5Tw0mFGjUajmXxy7Xj9AHjYMIyVwHrgAPAfhmGsMwxjA/An4Ks5HkPOCEfjBCNxyzXpDyQ7Xvl5Dg62+dK+t9UboCGlxlLC8co8s/HRfR3811NHuPknL/KDxw9bJSt6B6U4ypQvtbimiG5/mP40LlVq1XpFdUrboMMdfquJcSbh1dQjhddojhfAeYur5JizFBnVxW66TMfLlSdwOx0UuPLIcwg6fDI5vjSD8Nq0sIJY3ODxAx1UFydXhX/XuQu4cEm19bqdJ/oIRmJZl5KYTM5eWM6ly2umtbSCJbx0mFGj0WgmnZwJLyFEGXAp8HMAwzDChmF4DcOwW0BFwPgyyWcASiCpPC31f99QmGK3k2V1xRxoTy+8TnkDSaUkILtQ4x0vnWBeRQFv3dDA9x4/xEd+tQOA3kGz2XQG4bXErPt0NI3rlRBeKcn1KqHdF6J/KEKnL2T1ASwvdOHN4HgJwbBtS8eFS6ooK3Cxsj67iuU1ZtsgfzBKsduJEMLscenEMGR+VF6GNjtnL6xACBmSzFSCAWDzwgraB4Ic6x6kIH/qhVd9WQG/+tC5GZ27qUB9h5bU6hY+Go1GM9nk0vFaBHQBvxBC7BJC/EwIUQQghPimEKIZuIVZ7HgpgaREhvrfOxShvNDFyjmlaUs4GIZBa0rxVEiEmTL1fTzU4eOlY7285/yFfO+dG/jE5Ut58mAnJ3uGLMcrU1K2Chuly/Pq9odxOx1W8U6FqmTf5Q9xuFMKyGWm8CotcJmOX3JLnubeIeaWFeDOwi2qLnaz+6tXc8mykRPxFVXF+XT7w1afRoUSrJncLpCzDleYNcdqSkYQXmbS/+5m75Qn1s8UVswp4UMXLeLaNXOmeygajUZz2pHLzFkncDZwq2EY24QQPwBuA75iGMaXgS8LIb4IfAL4p9Q3CyE+CnwUoK6ujq1bt+ZkkH6/f9zLPtEvRcdgp6xntXPPPkr6DnG0OYgzZuAa7KTTF+aPjz5FaX7CifGFDdn+pruVrVs7rce9wbi5nP2UeQ8PW98d+0M4HdAQOsnWrc00mLWmfvbn560E/0Ov7aT78HDBEI0b5Al4asd+qn1HkrZ975EQxU6Dp59+Ouk9cUMWP92x9xDHjsgV9Bzbx9b2A3Q2S3H40BNPU+FJrO+14wFKHOTk8/J1hekPRDjS3A4Rw1qHIypFpyMWGnG99a4QB4Gov4+tW7em/exjcQN3HoRiBuHAYM6+dzOBkb77l5bA/p2d7J/aIU0pE/ntz3bO5G0Hvf16+6d3+3MpvFqAFsMwtpn/34sUXnbuBP5CGuFlGMZPgZ8CbN682diyZUtOBrl161bGu+znj3TDi9vYcu46frV/B/ULFrPlsiV8b9/zzK9w8paLl3D369uoXryWC5cmCmvube2HJ5/jsnPWsmV1wlUIhGOw9WG5nC1LktblD0X5xFNPcP36Bq57wwZAOmf/tfcp2ill/bxy2Pc6b7rysoxJ4Y27thItLGbLls3Wti/bcB6HnnuOFQ1lbNly/rD3VD3/OEVVtQhXHgWuZm669nIcDoF/zyn+b/8uVm04x2pyDPC55x/n8qU1bNmyflz7dCRaC5q478he/Hior3KzZcsFANQfepGmgV4aasqtx9LRV9bCU799lbXLFrJly8qMn/3mYy/x/JEeaqtGXt5sZyLf/dOBM3n7z+RtB739evund/tzFksxDKMdaBZCrDAfuhLYL4RYZnvZW4GDuRpDrlE5XXWlHhzCHmqUPRNV7pI9z6vbH+Jxs29iavsej8uB0yHSJtffv6sVfyjKey5ItCASQnDp8mpePNpD50CQovy8EWfiLa4pTgo1hqIGH/m/HYQicb7+1tVp31NdnE+XL8yRTj9La4utpHSVg2RPsA+EY3T5Qlkl1o8HlZvV3DuUEmp0JY0pE5sXyjDi3LKRG0dvMl83HbMaNRqNRnN6k+siPbcCdwoh8oFjwAeBn5liLA40AR/L8RhyxoA5i7HU46LE47Jys/oGw5QXuqgudlNd7OZg2wCHO3x87t497G72ArJ2VWpfPyEExRnaBt398klW1ZeycX550uOXLqvhru3NPPl656hlGRbXFPH0611EY3EcQnD7ayEOdg7x8w+cY81WTKWmRM4k7OgPcuHSKuvx8gK5Lq+tdIYqYppNKYnxoJL/o3EjKR9N5XiNJrzmVxZy78cuYPXckavLbzarwU/HrEaNRqPRnN7kVHgZhrEb2Jzy8E25XOdUooRWaYHL6rMYjcUZCEatOkxn1ZfwxMFOHtxziqJ8J5+/dgXnL65izdyytBXt5XKSHa8DbQPsbR3ga9etGlbQ88Kl1TgENPcGWDdvZEGxpLqYcCxOS1+Axw90sKMjxpffdBaXr6jN+J6aYjd7WvrpD0RYZhNn6Ryvk2YpiYVVuSlDYJ+NaHe8SrN0vCCRPD8SGxeU4xDSgdRoNBqNZjLRZaknQHIhTxe+YMQSIqry+Kr6Up493M0ly6r57s3rqS0ZOcxV4nYNc7zu3dmCK09w/YaGYa8vK3CxYX45r5z0jtrzcEmtFESP7Gvnu48dYkNNHh++ZNGI76kpcVvbpEpJAJQVphFevdnX8BoPVTbhVTIOxytbSjwuPnDhIjYsKJ+U5Wk0Go1Go9DCawIMBCMUm7WjSjxOBoJRq3iqKuvwN5ctYdPCCq46qy6paGcmSlJCjZFYnPt3tXLVWXUZhdWly2uk8BqlEOniaimcvvXwQSoK8/ngGueoLXHsLpPd8SpxOxFiuPAqdjtz1u5G5rA5CEbiSaFG5XiNVE5irHz1ulWTtiyNRqPRaBQ6ljIBfMEopaqGlEc6Vf0BmfNUboqPyqJ83rB6TlaiC0jKFQN46mAnPYNh3r5pXsb3XLpc1sEaLceroiifikIXcQO+eeNaytyjj0nVvPK4HMyzFUV1OASlHtcw4TW/sjBn/Q2FEJYQTFfHazqLjmo0Go1Gkw1aeE0AXzBizagr9TgZCEToG0x2vMZKaYrjdc/OFqqL3Vy2PHOR0fXzyrlkWTUXLK7K+BrFdevn8tcXZ18cUwkv+4xGRXnhcOG1oHL0ivUTQYUbk5PrJ9/x0mg0Go0mF+hQYwqhaAynw5Gx9YydgUCU0gK5C1VSfJ85y2+8wsueXN/jD/HUwU4+dPEinHmZNXKeQ3DHX5+X1fK//tY1YxqPcpiWpZn1WFbgwmuGVg3DoKVviC0jCMTJoMac2Vhic7waqwvJcwgac5TUr9FoNBrNZKEdLxuGYXD9fz7Pfzzyelav94USjleJx4U/FLWEV3nR+NwXtRzDMNjZ1Ec0bnDN6rpxLWsyqCt14xBydmYq9kbZvYNh2TA8ix6NE6GqSDleif27em4Zr33tDSzSTZ01Go1GM8PRjpeNlr4Ar3f4rPys0RgIRFlSk3C84gac8gZxOkTSrLuxoJYzGI6xv20AIeCs+tJxLWsyKC/M556PXciqNGMoK3DR0hcAsG5Ti8JONqp/pD3HC6AwX3+VNRqNRjPz0WcrG9uO9wJwvHt4I+l0+IIRa0adcr5O9g5RXugad4K5Wo4vGGH/qQEWVxdNu6jYZBYUTcXueLV6TeE1ZY6X/upqNBqNZvYxaqhRCHGdEOKMCEluP94DQKcvhD80vHq8HcMw8AWjVq6RyvVq6hm0iqeOB7U8XzDK/rYBVo1SZX06Ucn1hmHQajpe88pzU8NLccmyaq46qy5phqVGo9FoNLOFbATVO4HDQohvCyFW5npA08n2470UmP35ToziegUiMaJxIynHC6C5LzChOlZKeLX2BWjpC6QN8c0UygpcxOIG/lCUVm+AYrfTEqC5YlldCT97/2bdR1Gj0Wg0s5JRhZdhGO8BNgJHgV8KIV4UQnxUCJG+ud8spWMgyImeId68rh4YPdyoSj7YZzUChKPxCTpeUrSpsOequTNbeIEsotrSN8S8ioKc1fDSaDQajeZ0IKsQomEYA8C9wN1APXAj8IoQ4tYcjm1KUULn5s3zgWThdazLT7PZDkehSj7Y63gpJuJ4qeVsM8OeM9vxUo2yI7T0BXKeWK/RaDQazWwnmxyv64UQ9wFbARdwrmEYbwTWA5/J7fCmjm3Heih2Ozl7QTkN5QVJwutv7tjJzT95MalYaH9AOl7K6VICDMZfw8u+nNda+qkpcVsFTGciyvEaCERo9QZynliv0Wg0Gs1sJxvH6ybge4ZhrDUM4z8Mw+gEMAxjCPjrnI5uCtl+vJdNCytw5jlYVF3EMVN4dflCHO7009Yf5Gt/3Ge9XjleiVmNCcdrMpLro3GD1TM4zAiJtkjNfUP4glHteGk0Go1GMwrZCK+vAdvVP0KIAiFEI4BhGE/kZlhTS49fiqvzFlcCsKi6iONdfgzD4OUTMgR55cpa7tvVyl9eawNgQOV4mUKpwJWH06x2P5FQY2F+nlU1fyaHGSHheO0/NQDkvpSERqPRaDSznWyE1z1A3PZ/zHzstEGJq/MWJYTXQDBK72DYmun437eczbp5ZXz5vtfo8oWG5XgJISy3aiKOlxDCqlE1kxPrwSa82kzhpR0vjUaj0WhGJBvh5TQMI6z+Me+PX1nMQHzBKAsqC1nbUA7AohrZeuZ49yDbzBCkx5XH/7t5A4OhGP/+0MFhsxohIcIm4njJ5ZjCa4Y7XoX5ebjyhOV4zavIbQ0vjUaj0WhmO9kIry4hxPXqHyHEW4Hu3A1p6nnH5vk88/nLyXfK3bHIbLa8u9nLwfYBzjWdsKW1xfz1JYv4/SstPP16F3kOYdX9goRgqiiamC4t8bgozM+b8U2fhRCUFbgYDMdwOx1UF59Welyj0Wg0mkknG+H1MeBLQoiTQohm4AvA3+R2WNPLvIoCnA7B73Y0YxiJECTAJy5fypxSDy8e66HE40yqW2WFGgsm5njNKXWzfl45DsfMr4mlwo0N5bqGl0aj0Wg0ozFqmXHDMI4C5wshis3//Tkf1TTjzHOwoKqQQx1+8p0O1s8vt54rcjv50pvP4pN37bJmNCrU/xPJ8QL49tvXT+j9U4klvHRivUaj0Wg0o5JVfxchxJuB1YBHuRqGYXw9h+OadhZXF3Gsa5AN88uHtae5bl0995humJ0Sj4ui/DwrZDleZnLtrlTsjpdGo9FoNJqRGVV4CSF+DBQClwM/A96OrbzE6cqiaplfZQ8zKoQQ/Pz95wx7/K0b5tJYdWYlmCt3Tzet1mg0Go1mdLJxvC40DGOdEGKPYRj/LIT4LvBQrgc23SyqLgawEutTSedqXbq8hkuX1+R0XDMNHWrUaDQajSZ7shFeQfN2SAgxF+hB9ms8rXnT2jn0DYU5f3HVdA9lRpMINZ5ZTp9Go9FoNOMhG+H1oBCiHPgP4BXAAG7P5aBmAuWF+Xz88qXTPYwZjyohMb9SO14ajUaj0YzGiMJLCOEAnjAMwwv8XgjxJ8BjGEZ/Ngs3BdvPgDVIwfYh4G3AdUAYOAp80Fy+ZhZyw8YG5lUUUl+mhZdGo9FoNKMx4vQ7wzDiwH/b/g9lK7pMfgA8bBjGSmA9cAB4DFhjGMY64BDwxTGPWjNjKPG4uHxl7XQPQ6PRaDSaWUE2dQ+eEELcJMZYHVMIUQZcCvwcZKshwzC8hmE8ahhG1HzZS8C8MY1Yo9FoNBqNZpaSjfD6G2RT7JAQYkAI4RNCDGTxvkVAF/ALIcQuIcTPhBCpPXA+xBkwQ1Kj0Wg0Go0GQBipVUAna8FCbEY6WhcZhrFNCPEDYMAwjK+Yz38Z2Ay8zUgzCCHER4GPAtTV1W26++67czJOv99PcXFxTpY90zmTtx309uvtP3O3/0zedtDbr7c/99t/+eWX7zQMY3O650YVXkKIS9M9bhjGM6O8bw7wkmEYjeb/lwC3GYbxZiHEB5BO2pWGYQyNtgGbN282duzYMdrLxsXWrVvZsmVLTpY90zmTtx309uvtP3O3/0zedtDbr7c/99svhMgovLIpJ/E5230PcC6wE7hipDcZhtEuhGgWQqwwDON14EpgvxDiWuDzwGXZiC6NRqPRaDSa04VsmmRfZ/9fCDEf+H6Wy78VuFMIkQ8cAz4IvAy4gcfMfP2XDMP42BjGrNFoNBqNRjMryapJdgotwFnZvNAwjN3IPC47uiqpRqPRaDSaM5JsmmT/J7L4KchZkBuQFew1Go1Go9FoNGMgG8fLntUeBe4yDOP5HI1Ho9FoNBqN5rQlG+F1LxA0DCMGIITIE0IU6sR4jUaj0Wg0mrGRVeV6wN6IrwB4PDfD0Wg0Go1Gozl9yUZ4eQzD8Kt/zPuFuRuSRqPRaDQazelJNsJrUAhxtvpHCLEJCORuSBqNRqPRaDSnJ9nkeP09cI8Q4hQggDnAO3M5KI1Go9FoNJrTkWwKqL4shFgJrDAfet0wjEhuh6XRaDQzgMf+CerXwZqbpnskGo3mNGHUUKMQ4uNAkWEYew3D2AsUCyH+LvdD02g0mmlm16/h9YemexQajeY0Ipscr48YhuFV/xiG0Qd8JGcj0mg0mplCyAcRndKq0Wgmj2yEV54wmyqCrOMF5OduSBqNRjMDiIYhFtLCS6PRTCrZJNc/DPxWCPET8/+/AbT3rtFoTm/CZhUdLbw0Gs0kko3w+gLwUeBj5v97kDMbNRqN5vQlNCBvI7pJh0ajmTxGDTUahhEHtgEngHOBK4ADuR2WRqPRTDMhn7zVjpdGo5lEMjpeQojlwF+Zf93AbwEMw7h8aoam0Wg004gWXhqNJgeMFGo8CDwLvMUwjCMAQoh/mJJRaTSaxInfXTK94zhTsYSXDjVqNJrJY6RQ49uANuApIcTtQogrkZXrNRrNVHD/38EfPjrdozhz0Y6XRqPJARmFl2EY9xuG8S5gJfAUsnVQrRDiR0KIN0zR+DSaM5eBU9B7fLpHceZid7wMY3rHotFoThuySa4fNAzjN4ZhXAfMA3YhZzpqNJpcEglAoHe6R3HmooQXBsTC0zoUjUZz+pBNAVULwzD6DMP4qWEYV+ZqQBqNxiQyBEO92m2ZLizhhc7z0mg0k8aYhJdGo5lCokGIRxKFPDVTi32/6zwvjUYzSWjhpdHMVJTLMqTDjdOCKqAKWnhpNJpJQwsvjWamok72Os9retChRo1GkwO08NJoZiLxWCKhWzte00OS8NKOl0ajmRy08NJoZiL2E32gb/rGcSYT8kOeW97Xjtdweo7Cjl9M9yg0mlmHFl4azUzELry04zU9hHxQXCfva8drOLvugD/9PYS1KNVoxkJOhZcQolwIca8Q4qAQ4oAQ4gIhxDuEEPuEEHEhxOZcrl+jmbVE7Y6XFl7TQsgHxTXyvhZew1FOrK9teseh0cwycu14/QB42DCMlcB64ACwF9mO6Jkcr1sz3Qz2wGNfhT33TPdIZh/a8Zp+Qj4oqpX3tfAajhZeGs24GKlJ9oQQQpQBlwIfADAMIwyEAa/5fK5WrZluDANe/C94+j8g1A8LL4J175juUc0u7DlFM9nx6j0mxYm7eLpHMrkYBoR9UKyElw6nDSPglbcDs0x4Bby4g13TPQrNGUzOhBewCOgCfiGEWA/sBD5lGMZgNm8WQnwU+ChAXV0dW7duzckg/X5/zpY908nVtlf0vsL6Pf9Mb8UGXK4B6Gln5wzcxzP5sy/z7mejeb+35Qh7cjDOydj+i557D60Nb+TEolsmZ1BTyEjb74gFudSIc6InQCNw9OBemofSv3Y2Mhmf/aauZkqAo7ufo7m3ZlLGNRUsO/QT1vS+ylbP7BnzZDOTj31TwXRvfy6FlxM4G7jVMIxtQogfALcBX8nmzYZh/BT4KcDmzZuNLVu25GSQW7duJVfLnlReuQO6X4c3fGPSFpmzbd/VAnug8n2/gse+Ap0HZ+Q+ntGf/ZEY7AbcpVQWkJNxTnj7IwHY6qOxOErjTN2PIzDi9vva4VloXH0uNP2OJQvmskS99un/gPxCuODjUzXUSWdSvvu7owAsqS1M7JvZQNtPiXQOzNzf/hQwo499U8B0b38uc7xagBbDMLaZ/9+LFGKzF8OAg3+BeHzq1/36X+DV3079eseDqn/kLgFXkQ7TjAeVU1Q6d+bmeKlQ0+mY46O+w55yWVLC/h3edx/s/cO0DGtGYYUaT03rMMZMaABHPDTdo9CcweRMeBmG0Q40CyFWmA9dCezP1fqmhKYX4O6/gqbnp37dAS8MdcvCmjOdJOFVoIXXeIgG5W1pQ+IEN9M4nZOrVbsgd7H5HQ4kP+fvnJ5xzRRi0cQ+mm2ff9BLXjw8PRfQGg25n9V4K3CnEGIPsAH4VyHEjUKIFuAC4M9CiEdyPIbJQx1ghrqnft3BfjDiM9f9sBPsB1ch5LlkSGa8dX72PwA/vlge5M80lFgta5ATFGbiPgh65e1Am3SDTydCZoNsd4n8LtsvHoID4O+Yum1+5Mvw0BemZl3ZEuxP3J91wssce1TPVNVMD7nM8cIwjN1Aaq2u+8y/2cegKbjsB52JcvgxKKqBuRtGfp1a52BnorbQTCXkkycskCetaEBeXTrGqPMPPQLtr8mTXFnD5I9zJmOFGs3tDvTNvM9dOV6xkLxfWDm945lMklxbT8KBjMdNp8eQv8mC8tyP5cSz4MjpoXrsKNFdMlfmwxkGzJaZ6kHTqYsEIL9oeseiOSPRlevHwqA5BVn9cCeDv3wWnvyX0V9nCa9RpkEbBkTDEx/XREgVXjC+q8uOffJ2tl1RTwbDhNcMdDrtIdDZluczGknCqzDxeYR9gOl0TVW4cagPIsGpWVe2KNFde5bsKTrUM73jyRZLODP+FIjpPr5qZj1aeI0FS3hNouMV8ELXoZFfE4uaB3zAP4rwevVu+O6K6T1Q24WXuqIca7gxHoOug/L+GS286uXtTAwx23tI+tqnbxy5wBJepcl5ivaLrsEpEl6B3oTjNlNQorv2LHk7W4R32C9TNmB8KRAnt8G/zYP+1skdl+aMQguvsaCu6iZLeBmGPMD3n4TwCOXNQmM42J96RR6op+qkkI4kx6tA3o716rL3WOJkM9MKNA71wp8/m9vm1ZEhcHqgsEr+PxMdLxVuAvDNkhNvtqgLnfyU5Hr7b9HfkftxRMNSLERn2Cw8y/FaJW9ni/C2H7vH43h1HZCh9e5RLpY1mhHQwmssKMcrlCHUGB4E3xgOxpEhMMxZit2HM7/OfoIbLbzhPSlvRwtJ5pLQgHQKIBFqHOtBToUZYead1Lf9GF6+HY48kbt1RIPyhF9g5k3NVMcr36xYP9XiOB6HvqbcLT/kA4cLnO7k5Hq74zUVoUYluGdaIrg6JtUp4TXFv1Ff+8gXq5mwH7vH0wZKXXxPhejWnLZo4TUWRkuuf/pb8Itrs1+e/SDe9foIr7Otb3CUGZXqZDQ4jTkXId/kCC/hkBMPZpLjFQnAyz+T93N51RsZAmdBImF9JjpeAa9sqVNYPfUn3kMPwX+enbsQl3JthZhex0sJ7pnqeFWvAMTU/0Z/djU8+c2xv2+ijpf6PGaLw6eZkWjhNRZGE179LdJxynaaucojAVmVPhP2JOaRQoiGMfmOV3gQfvseaH45+/eEBmw5XqbwGms+Red+qFwCFYtmVo7Xnt/Jq94898hieaJEAvKEn18snZeZ6nh5yqGkfvQT0Qv/BS/9OPmxXXfC1m+Nb92+NohHc+d6pU4QUcJruhyvmdakO+CV3838QnlxNJW/UZWecWrX2N87FuHVvB1++97kUi5aeGkmAS28siUakvWUIPOsxuCAPBlkCkWmEhqj41Uyd+SD/VAPREz7fbKE17PfhQMPwut/zu71Km8tdVbjmB2vvVC3Gkrm5P6g3vpKdvlahgEv/Q/MWQtLrsix8ArKfSeEdL1mouMV9MpyCqX1oztPO34uw7N2dv4Cdt0xvnWrySP+HJ0Ak1xbe3K9V96Wzps8x2uoVxZnzvQcyJSEmVTLLdAHBRXyfmn91AovdXE50sVqJoJjCDU+/s9w4I8w0JJ4zAo1auGlGT9aeGWLNV1aZHa8lJDK1p1Qry+sHjlspdZXvXTkUKPXdvU/GUVee47CC/9p3j+S3XsiQ3LW0ESEV8gPfSek8Cqdm9swRiwKv3gjPPf90V979Ak50/L8j0PNCrlPcnUyjAwlJiYUVM5cx6ugwnS8RviMomHpTPUcTTif8Th07B9/GQJ10hxLTuVYCPlk1XpIH2qsXjpx4RUNSyfwhxvkdzCdeLUL7pk0szHolW4nyM9/KkONSngN9YyeepGK/dg9Uo7Yqd3Q9Jy8b59Jrr6vufreac4ItPDKFuUglc0fQXiZocNs3Ql19TXvHHlSylQfRq2vapkMNWYKZdrDLmM9IKXj4S9CXj40bJbjywa1TR7TLRhPqLHzgLytWy0P6mFfclhW8eDfS0duIgx2yhOaPZk/Ey//HIrrYM1NUnjFI9B3PPPrI0G4/UpZCHasRAKycCeYjlcOZ1COl4BXnnxL58rfR6bvr7fJnERiJEqEeJukOxsZGl8YTQl5u+DrPQ7/ff7k5H3ZXVunKbwMQ36/8/KhfMHEQ42/fhs8+uXEzNV0Qm5oHMLLMOAXb85tP8lAX6J4bEn91Ob42Y9zY3Wdk0KNI3zvXvqfxH17esdscLz23AO/fvt0j2L6+Mvn4Nn/N92jGBEtvFJ5/WFZLT0VJbyqlkghkK5nohIdQ1meJNXV87zN8sTUeyz964JeEHlQuUgWK8wk/NSVYNUozlg2HHoEDj8CW26DhRdI4ZVNbzN7/SMYn+PVaYqg2lXypA7pcyqOPA4HM4RA43HY+X+jn9TVlXo2YYuu12HhReDMN5OKGfnA3/QctO6Aw4+OvuxUooHEviuomD7Hq/swHH1y+OPxuBlqrJDhYMjsANndUiVw7UJ3PNumRIh9nc3b5XT/tlfHvrxUhpVEMcx0A3PGbnGdPCaMt3eqYcDJF+GcD8MNZu5bOvdvPI5Xf4v87u39ffLjhx4d/n1temF8+yvgTQiv0rly7FM1AcB7EjCr5I813Bjql8IZko8N/S2w+zfyYmnglNx3Z10nn7OnbdhzvGZqm6wjj8ORx2Ze0d2p4sgTcOK56R7FiGjhlcqDn4Rfvhk6DyY/rmYJVi2Rt+nyuNRj2TpeSqTMO0feZjqIBPvBUwZFteZYMuRveZvkibBy8cRzvF7+OZQtgHP/Rgq5WCg51yET9orfMD7h1bFPJu6WL0yc1NOGYfrkST3dAbB1p/wsX7tn5HWpK3Vv8+jT0we7ZCIxQM1yeTvSgV85XePJBVPJ9TC9OV5Pfwv+8DfDHw/7ZEi5oFzmHkLmcKMSXg6XnDQBiVsYX7jRcrxsgrz/5MjjGAuZ8hSDA9LNLao1e6dOIFQaj0LZvITjlU6A2i/isj2Rqu9by8uJ30Y0BL97HzzypcTr4nG45wPwRBadM1Kx53iVmEV+pyrh3NsE1cvBVTQ+x6ugkrhwJvJhQc5Uvv9v4b/OgQc/JQX1FV+Vz6lQYywihVt+ifwupHPhZwIq5WQ6azlOJ2H/+EqNTCFaeNkxDHkgDfbDr29KPtkrIVNpCq9U1ykeT/wQs72CVw5Zw9nyNtNBRAkv1asvo/A6KUMghdUTc7xiUXklvPRK6e5ULZWPZ5PnpcTnsAKqYwgndeyXFbEdjswndVVYMtif/uTXY9ZFO7V75HVZJwtj5FpqkaDcNvUZuEtkO59Mn5lhwKGH5f3xCi9nSo7XdFxh9xyV+zd13Sr0WVCRqK6fKcTXc0Ruw5y1Nsdrb+L5cQkv8/tkd7z6zQuDyci/CfvTf4ctx6t2+PrHgvU7KU2UDEl33BiP46UuBvwd0N8s77fskC7q8WcTJ6W23fI14wljp+Z4wdQl2HuboKIRqpeNT3h5yojluZOPSQGvFHIFZdKhXvlmeXHlKUsIGHubJBj5sx/sgX9fAMefGdv4JgMV+ZiqllZ2fnQRbPvp1K/XTkgLr9lFyCevQtfeLA8sd74jYZ8Pdskr9vL58v9U4RX2Y/Vwy9rxGpDOjrtEuksjCa+C8oTbon5Qra8kT9H3npQuUVG1TK4f74m67VXpaCy6RP5vCa8s8rxSHS9HnqzAnu0PwTDkSVlVxM50UrefLNIJQvXYaGEU+3JHmuCgxK5yHUFedWf6zLoOys+jaqk8cI81nJbqeMUj5ndsiuk7LsPgqd93VeLEU56F43VU7oe6VTbhtV/uP5iY8EpyvEzhNdH8m1hUOhr5qY5XwHYRVGeua5zCS+1PT5nch8KRIdRo+55nG8rreh0rFNe8Xd6eeFbexkJw7Gl5XzmyY+3EEQlIEWif1QhT1zaoz7zArFk59lp6wQHwlBF3uJNd+LBfiumPPg1/dTe85Xvy8aKaxPFWfT5W0dgRvmcdr8n9msuZz+mIhhKfw1QXeY1F5LF7PLNNJ4t43Mwd1cJr9qAOcosulT+8jr0yZAVSyBRVywMlDC8pYQ89jmVWo8qFqlmR+Qsb8KYPNb7wQ3j4CzJPSdXwKl8gDxbR4PhP1CfMq7RGU3gV10mBOCbHqzTxmH1W2Gh0HpCiV7mA+UXgLht+Us9WeHXsHXnmoa9Nbp/ISz5IBlIaE6ur3mKb8KpZKV2ydLlvyu264BPydqwnCLvwUie4TN+raDg3OWABb2I/p15M2B2vwkqZNzNSqLFqKdStkb+jviboPZr4fo3HcVHfp0BvQpB4TXdnoo5XODVcbmt7pUKNluM1TlchaPudOBxmHl8a4TXUm3CWsq1e3/U6zD9XOqYtZv29489C7WopJg+bgkt9R4ddRI4SRlOi255cD8OFyFDv5NcfC3hluK9ioXSkBlozl/dJR7AfPKXS8bJP+FGhZUcerHhj4vMtqk1EDyzhtUbe2rc3ddvV8Weqw5H9LSSauE+x8FLHoOnMLVOCSzteswh1AiishMaL5X11hT6YKrxSDlb2H1i2JxJ1EAdTeB3OkLRvXmUXVgEicbBXRU0PPyofiwalBV9UbY55nHlex5+VokIdfISQeWPjcbxAWvjZ5nipk8GyNyQeS1fLa1ThdRQcTrlPRroCGzglxWrVksSMu3gcfroFHv9a4nUqz0O5jiAP/JHB9Llvhx6BOetg8Rb5/1iufA1jeDkJyPy9ev4H8ONLsl9+tthnbKZOGFH1rArK5fejZE76kgIhv/zsqpYkXMy9v5f5UQsvNJc9AccL5AnGMGyhxgmGvIblKaaGGu2O1ziFV8jmeIH8bWdKri9tkPezCTUahvy+166SFy/N2+W4W7bDksth6RVw6BHyQz0y1OgqlJ+l3R3/0z/IosmZsItudZtfAs3bEq8JDsiw08O3jT7msaDyl8oXJCa4jJQikIp5LI07PMnfIXvdNjvFNYmLLiUs1PdYOaundsN3V8DJlxLvU8fKqXap7SWF/OM8/o8X9f2dzvZWIXN/a+E1i7AfUErq5ZWmJbzMxOpMwst+1TWWUKM6uFcvlwdWFZ9PWrYpvPKc8gA92CUFgzrhH3ok+YCkxMF42gbFIvIA0phyIq9aOlzgxKIyKfWBTyRcpbTCqyD7H4ISLGo2I5gFOjMJLzFcEMbj8rElV8j/R8rz8rVL0VC9POFKtb8q64gpIQaJg2+S8Fopb7tS3KyhXnkSWn6N/DycnrE5XrEwYCSHGiGzQOk5LL8Lmco5jJdem/AayfECGW5MJ3jUTN2qpbI8CCQmPNRvkL+x8QivaEC6lCAdrkBf4mp3olf66uA9UnK9u1heUEzU8VIXXoVVw/exYcjtUqG8bJyEwW75npoVctJO+x55IRULSyd/2TXga2Nhk/kZrHyzfM4u6nqPQU+GGdaQEN3KiRMCzv8Y7L8fml6Ujz3zbTlxpX1vmgVMAHV8LF+Y+P2NJbQVGjBzvPKTLwbtx2I76UKNFY3yN61cLiW4VFgXps/xUvtHOKbe8QrMAMcrbBNeM3XWKVp4JWM/mQghLWW741VYnbgqSp3VqP73lGcO+7S/luxa2K+yKhrl7UDr8PcF+xMHuaIaKbxUCKF+Axzbmjixly+YmOPV+oo8gS1KI7y8TYmT+7Gt8KML4c+fkdXHlfALDcgQR54r8d78wuxCDkO98sp8eUq/y3QndXuia6rw8p2SJ+bl18gQ6Uh5Xr42ufyaFYlaair3Jd3kCnuo0briTjnwH3lCOjrLr5Whi+plySJuNNQJQZ3wlQhN990AWysrb/bryIYkxytVeJnrUt/LTNXr1Qmoaqn8XhbXyRmNzgJZHiWT0zMakYCcEQjSeVBJ5NXL5YlyvGUeII3jZdZTCw/KMKT6zRbXTk5yPaQvkhsyO2Gozz8bx0t9z6qXy3BjPCpTEkQeLLgAll0NCOaeekTWJFxwgXy9/UIy0DfyxWOq6Aa4+NOymv9fPifz9176kVznSHXuFP7O7OsE9tkuMCsaZYg729+WYcjtdJcOz/GyF8y1U1Qrf1fRcOJ7Wlglv8fqs2/bLW/tJVLU9oSm2PHqa5JOf9WyaQg1zgTHSwldI/mc42tPpCLMALTwsqMONuqAUrdK5hzF42aosSZxoBwWajQPpBWN6Q9ahx6Bn1yWXNjNHmrMJJaiIflFVk5bsXkF1rxd9gu89HNSKO3+jXxezWpMt6xsUPldCy9OfrxqqRQTfSfkl/jOd8gr5Qs/KZ9XJ92QL7FNimxDjUceTwgWO6X1cp32k6k6+M/bLPOF7HlW6mRfvVzOpFMHxhTyomboqLReiihVS80SXq2JqyZ/lwynKBcKoKhKHoRTD/zHtsoT6VwzT616xXBXbCTUAcNpnvDL5ssTTKYcO/U5T3aR1d7jiTGkc7zy3In9ofo1pl5lqhNQ5WJ5q1yv2pVSlBZWjr+cROUied/XnggzNpg18cZb5gEyl0RRrqf6fttPvmNlmOOVZj8oIVYyBuGlLgJqVsK8c+X9E8/C3A2J3LSGTQji8sJE5WmlCq+wP7NzkZrjBfLi6ppvyqTy/7tO5mae/7fmLPFRcrAe+TLcfnl2M7G9J+UxuKBCRgAql2T/24oG5THLU0Ysz5MivPzpHS81i3moW34e+cVSiNv7k6oLO1V/MBaRx0lI5AtOFd6T8oKktH7qZzWq7+9McLwg+fP982fg3g9O/XgyoIWXndQrubrV8ofT/boUN0XV8seeX5I51FixcHg+TOtOWS/HiCVOEJBcK8gKD6YcfOyzn8BM9uyUjtfcDWbJhwJoel4KrvyihIjLtm3Qc9+XQurUbhmWqFsjRYUde0mJ7bfLg8t7/wAbzVwQdRAKprHs7b3uRuLQw3I/zN2Y/HhJvdx39n0T6JVX1HM3ygOq3Q1SAqVyiXQE219L64Dkh80DhXK8QJ6kTr0ixxH2JwT1YGdiv9qpWTk8f+vULmjYJJOmQS67/2T24VYlvNQJ35E3co6d2i9jEV6PfoV5zfeP/Jq+E2YisRjuxqg+jcKcPVdSL38jqb+LniPSCVEdDFR+TK0pwAqrxjcxIBKUJxjhSBZe8zbL24nUlMpUEkUtM8nxGm+osV+OPd90WZTzZxeuSuyOyfE6JI9PpXOlaFBOuj11YPk18nbZNYnjihJTKrxpX38q6RwvgFVvleHMoW64/MuJ+oR21+vJb8LT305+30Cr3B9PfH307fM2yYtL9b0baVJSKpbYTVNOwn4stqMmNPk75eej8i1L6uT3ITwkL7zy8uVxIGY2bjdiieVOJd4mc2b7BNzY8TIjHC+b8LKLMH+ndGJnSPhRCy87qpaL0y3/VycHVYtFnXg9pZlnNVY0SrGmQnJ9J+DOm+V7a85KPlDbZzWqH3RG4VUub4trZU7Lqd3ywOYqSCRwVyyUt64CeUBPdwXZ+spwobDvDzJB/6eXSeGRmt8FUGU6Fu2vyYbHK98sxYA1o8nmeKUewPILR28ZFItKx2vZNQnBokhdByQKOFYtk//b3aCeo1K0lNRD/Xop+tIk4LpDylGYI8OBAC/+t7zd+F55228KOn9ncphRMWcdtO2RQhTkwbzroFyvQom6bPO8LOFlc9fS5diBWXtOzboag4DZ+3uqu18e+TW9x2VSfEH5cFFnL6AJCfepN0Uc9hxJFB2GxIywuokKr4AUGMV1MtToPSnduXQzzsZKJsdLLdNynyfgeKmcIiUgCqukG2M/WagLOEt42cpJ9BxNzilSdB2U32W1XOV62VMHNn+I4423yGR7dVxRx5nQQEI0ZHINg15TNKb8zoWAG34Eb/gGbP5r23fCJrx23wkH/5T8vsEuQMArv5LHp5FQJXMUNSvkMTYbl8V2ERt32GY1RkOyzEamHC81xkBvIt+yeI787Dv2JVz6WFh+39Xv1F069aFGNbNdXRRMpdCYCbMa7b8f+4VuyCcvDKeqyO8oaOFlJ/VkogrlHdsqb9WP0FM2PJ8m5JMHo7L5iWUB7PylfO17/iAPEupAHYtIQaAO4nlOM88jg/BStn5RtbyiiIVkDgfAcnMGYPmCxPuKqtOHGu/7m+EzjbwnYe074KJPyYPpquuHv6+gQjpqL/2P3LYLPm7ui1Ip8lTyezrh5Soc3fFq3ia3VW2LHatOkC3PyxJeaYq79hyRbpfDIV1BSJvnZQmv0rnSKSxbIK/OSxsSsypVCFWFmlOZt1l+HqogaMc+eeJS6wVbeyFTeI12MEwrvJbIMGiqcxcaMJPxyd7xikXA14YrMkL9pmhIOhEVi+T3clio0Zs4acPwbQS5nT2Hk4XXwgvk8pQQmEio0eWR4sfXIR2vsnm29kUTOMD2HZduqroYUp+D+u3aQ41B7/ha5Zj1pCzSFVEN2C4MINmhefpb6Wcedh9KJJ0DrHyTPCbNPz/xWFE1TY03yzzMVOFl/w5lEsSBPvm+1AskkJ/BhbfK41mFKbyU4xXok9+p1Nl2g12w7mb5+/rL5zK3JjMM6SbZj3PVy6XwydRuzY5NeEnHyzwmWZMpMsxqhITjpboMlNTJ397JF+T/G26Rt537Esei+vVTO6sxEpDf0fKF8rsZDUyt46a+LzMix4vh5UIgu5JIU4AWXnZShZe7WDpYqu9TkvBKE2p0lyQOoOqg2XM0UWW5uC7heKWb/acS55PG5E2sE5ILeKqr2WVm6EAd6KxlpYi4WMScsWRzJYIDcrvr1sDVX4cvnkxM80+laok80czdmEjKheRyD+mmZWcjvI48JgvULr58+HNWgc40jlfJHOlS2rep52jiZF+1TIZi0+R5JUKNprBTztTyaxKJ2yqEOdiZXngp8duyQ96q9dgdr8rF8kTe/bp00P5z0/Dqzr99Lzz1r/K+lVxvE16VS6TAUknkiqTwa5bCa+AUGHFckRFyb/qaAMNMgE+T+B3wDne8HM7ksM9Qr/ydKHEM8rfwheMy9w6kuIkGxtZEPRaVBWVdhfLzV8n1ZfMSZR4mUsvr0KPyN6DCo6qDQLpQI4wv3KjKUiistkE2EWrleNUDIlngBfvlSdYuYoL98neo2lkBrL4R/mFv+sRxsM3S9srbJOGVQRDb+zSOhKdUbpdyvDrMNlGDXYmLj1hUrrNysTz+tO5IlJRJZahXuhbK2YeEq2YPZ3a9Dv+xNLmZNiSV8JDJ9aZAUHlY+Wn2kb12YpLwMo8Zhx6RF6RLLpe/8Q5TeBVUSAE0lcJHJY9XLBx7uZN7PyRL0qTry5otVo7XGIRXfwt8e3FiNuxY+d9rE1EKSHG87PfNzyHVkZ8mtPCyE+gbfkCpXZ0II6ofnacs/axGd2niKlkdNPuOJwRRca388at6QJAsUorStPqxpm6r8IZ5ICibn3CCyhrg3b+D8z6WeF+6tkHek3KWU39LIjSmph/bD2aZUCfQCz6RCGWAmWg6mvAa5cfY9qqczJCamA9S8KhcHoUSXkLIMKi6klGJrWqseU55kj/woKxP9JfPWQdkd6hHjlWdlJTwWnaN6TIIKbxiUfl5pgs1ls2XYQcV9jm1W34HlPMJZtulJfI1d75d/vjtLXNA5gGqOkgqlyc11AjDr9jGI7zMfChXxJfZXVAnskyOl8rxUuS55BjtYWz7jMZMpBMcoxG1OYLFdYkcr7J50gXzlI/f8fI2S9dC5UGBdHacHpvjZQs1wviEl31iDaTv16j2uadcrt/uJKgwikrohoTbqNzHbFBjGE14ndoFu+5MvCY1vysTFYsS3yU16y8eSazPPlNw5Zvl/UwnR+8JeWt3vNSx1R7OPPmSFEqpKRXqYtltFlCNhaSDnO4iWOEulsevwS752VihxrrEuurXy/SU6uVSXKqCwe7iqQ012ksKjaWlVTQMB/8sP587bpSpMWO5EFKMJ7m+Y59837PfHfv6Btpko/lTuxKPJeV4mb8Rw9CO14xmqHf4AUXlokDC8XCXpi+gau+7FuiVH3jvicRVmf1AnTqrCTIIr9TkenMMKnFVsfwaaX8nLSvFPVOukBFLCC77j3U0ll8r88lWvTX58dK5tlBjf4YcL1tdlVhkuBDr2J/IqUslzynFjcq3guSDvz3/SSW22k/2a26SYubAg3JiwLafAGaoUV25qu1bcKFMEM5zyc9roNUM/xrpHS8hYP45sgwGSAFZvz5ZmII8KJ94Vuaa5ZcMvxIO+RIncOV4OdMJr5STkv0zHqPwEsQzl6BQJ7LKRWZV9VFyvGB4C6WO1+RtzQhCQAmOsTQBt8/6LKmXvxl/hwwVQ/KMs7Giqrqnzqx1FSQ+n9QSMPaG39kS6k++QMnkeKn6fU53suOlrubtJQysGY1jEF5Ot/yejRZqfPF/4IG/g5Pbkvs0jkblInkMhGSRqJw6qxVXjVk53pVZhNtreCkKKqRzaHe81LEg9feQFGo0Z+vam12nE14gj6UDrfJi2XK8zPCvPa2gbrUZajwqf6/5xWYz+TSpBfH42Fs1jYaaSalCjZCd8Op4TR4fb/wxXHab/A2o38FYGE9yvbpgP/JY+iLTsWjmSUnqmGv/nNPNaowMyXA0ZF+2JMdo4WUn3clE9eVyFsg8IMgQauyXIsrueA31yB9eRRrhlW2oMTW5vrQBEMmhvnQU1Qzv12hX++pAZR3MGkdeHsjcr/c9kFyjCxKOl2oUni7HCyPh5Dz0efg/Wx7ZYI90KOoyCC+QboY9zGYPdVUtldsRDad3Wc7/GHzuiPxbcoV1UMkP9yYOoCDzjj70UCLEVNYgw3LpanjZmXeuPOj1t8jyI/b8LoVK+r7hR1C9NPkAoa7I1EEyXY5Xca0UbKlXbCon0FWUvXix78dMU/j7jstlFtXIiwn7smNm38jUk2/NCvk+JRCaX5bvt58oUxmP42Wf9VlSh9UiRYWH1Yyz8XDoERn2SnXpnAWJpHN7t4nC6kQqwlhQxwuF+i7b93OgNznPzD6rUTkSHTbR17FflvhQgjBbCsozCC/bZ6JyHf/yWXN23xgcL1Xct2OfnP0Htmrw5vevqEZerIxU1615OyCSLxKFgMrGZMdLnVyHCa/ErMa4w5xAFbHlQaXL8QIZblRuoj25XqHSCupWyeOQ75R0uN0l8oSfLs1i+0/he2uyK6GRLd6T8vMvrhubG6s6oCy8CC7+exld6BjHxYQS6rFw9nX0BtoAIS+iXvqf4c8/+134nwznOhVlUOk4YOZam4WV1THWfpF7JjheQohyIcS9QoiDQogDQogLhBCVQojHhBCHzdssf8E5Rk2jVj8shTphFlUnXAxPmfwR20WNmqVkd7zUFYjleNns33ShxsJq+T57b8Gg16yXZF6hldTBh5+AzaPUJCmqlmFFu6PRc0Tm4UDiQNXXJE+wqds9FkrqZfigv1keaNIKLxInzK5DshyGOtirK2ElctNRPj9RMiAWMa8+zTFXLZUnxcOP2oTXkvTLWX6tfE33ERlqtFfIT6V0rnTZ1MErneMFCfdx5//J/VC/YfhrLvg4fORJWPcOuX+SkkAHAUOecOxuoNpvYIZUl6QJNXYltjdrx8smvDKd5HqPy++tEPLkH/YnZupadZxSfro1K+Xnr8bY8rIUpanun510SeWjYRem9hOgEl5qxtlYCQ/KBtLLrhk+ZiWCnZ7ErGchZGuxE8+OffZYcCD5t5+uUbY9tOV0J4dw0oUam56T30VH3tjGYr+QtGZSzksWgb5T8vjUvkce17LJ8QL5HTLi0lnvPADzz5OPq9+UEh5qxni6fEKAbrOMzYZ3D09HsIczYWTHy+EEV4EMNYLcj5bwypAHV1wrJ4lA4kKhsFK6c5D4vatzBcicTLW8dOHGo0/KY9iO/02/zpEI+WR5ojveJv+e/Ia86PU2yeOk6v3pcCZ+Bx37ZJpFur61LdvlBX1Zg/yeVy4Zu4sbDUuTQc10zab0CcjvVVENrHsnvHr3cCHa9LzcrnS14FQR8VTHSx2n1W9Efb5l8+VxbaTevVNErh2vHwAPG4axElgPHABuA54wDGMZ8IT5//QTHpQnzdSTSeViebC113DylMoTvd0CVQdSV6EUSkO9CXEzzPHqSLr6slDrsB/wVLsgO/M2DXedUklXF6zniCx/4PQkRKH3pMzvGunkOBoq10yVS0gXaoTE/lKhO5WQrsIl9gNXKmXzpN0fjw8/8S++XF4F//YWeOY/pFDIJCTVrMlDD5Ef7ksONf7/9s47Oq7rvvOfO+iFRCFIsIAFrJLYi2kVioIluUiOS+zItuJslM2uZSexUxx7rdjOrvfsnjQne1zWG0ebpmwcx46zkpV4Y8tFUJdIkSIpkpLYKYIEG0iiEAAJgnf/+L07782bN4MZcBqI3+ccnBm8AWbunffevd/7azepX22JFq+6FBav2WtkkNv2t/J7MLDeUT1VantBsqsx+PzimYCwqE58j2mLI1yNZ+W6mzIrcQC6ckkmqyh6u+QahdS13s4f9i0ntSFrTHCfxiAtXlD3mTfEinnuoLhh0zEui1cg+SDoXm/04uqcxStbMXT4aYn7CcZ3OZwIDltF2m+X6zIqq264z7/PgjgLZ/C+jsW8rObA9xC0eJVXhyxe3mR++jWxLgydl7Im4R0nMiEovIbOy0Jsyky/LdbK97nqw36pmUxdjW7sO/yUtHnhHfK7G5eCrkaILi9iLfzwc3K+7/5S8mc0t8s4NnpFvgt3LqKEV3UDGJPC4pXK1Tjdzxx258PtT1rd6FvgZgQWjtMWB3Y5CYUVWOuLhi0PR8dE9XWnXowcegr2PCrideC0jHk//a9+KQmQ66kuUGdu61/JZx1+Kvn9jm1NDF1pvSk5BnUs3NjQ4O0rmmmcV1+3zB83/7pc32Eh6gRgsP4liNBzW8EFjQuXBnwDh7MKu+9/1mrPQPBmZm3LI3kTXsaYBmAz8FcA1trL1toLwPuAR7w/ewR4f77akBWpigLGyqQadnPAghK1X6Or2G6MvMfQuUCAsudqqWshvsl1uEgjJNaMcUQJr0yIV8IPCq+DMjk2LfBFYfBmHS8u69D56MPtDVu8XP/c4HNqj6ymU7nyQFYro5fFRZG0T2Ar/MZWGZSvjka7+hxNC8Qy88q3iNnRsS1el/t9sVOfwuJVUSOC9uJp6ftYrp6qKYnJGcGBeeBUtMULAi7VQKzPxbMyWdU0JU402/4WvrExOqaht8t360a5Oq5eFUuos9SGE0ZS3SstSwAjAtydW5d5m4rqRiILtKYjmHwQFM5uM2lngc22Pti+H0lczvzbkl9zFq+wtWXBZnk88kzy//zkv8A3NyeHJVy+KAu38HuF3WyDAQt8WHiNDMrEemVY7uWjzwM2ugbfWFQ3+IsZF24RbMtwr3ze1Flwzx+LpceJ3LFw19DrP5DHBbeLZe9iwOJlynwhF1Ve5I1/kxp/Hb8XPUY0tYt1v6/LSyDyEofCrvdLfgmPuMUrU+HlcAsFkOt9/m3+orWhzc9UbV7oZ0mGq9efOyRtW/4BGQt3fy/5M7/9YQnJiKJri5yD//gT+MQzUjPtua9IfGnQrR/c0spdn6+GPqv/pAiRuYH7tHWFLBiySQxw5yy+oXuGcV79J2X+mHGDhIG88vf+awOn/bkiLLxOviqLpGlL5Np1i6zLA3KOy6uTXY2uMHcJxHnl0+LVDpwB/sYY84ox5i+NMXVAq7XWFWQ6CbSmfIdCEt4uKMj9/wDv+ar/e6Tw6vNv3NpmGTTPHZaLyg3aZRVy46ZyNUZtGzTcm7lZP0h426DLgzIwTVvsmeaPyMXqKh1fC3GLlye8UroaL4owchOim5xP700f3wV+lmBvV2Dibwx8RjVs+h349F6475Gkf09g6TvhzGvyPBjjlfSZ3iDSvUMsRKliQMAfuKIC68OEXY0Jwuu0DFqx8mSr5rTFgE2MZ3Gbt9c0JcY69ByUyf3FP098D2slc8+J0yhLU/8JGdSctSJcIiW8T6OjokYWGWde9yaH8uRdCMKUlct5DLfj6lX47gNw8Mnk/wnuZVk3AzBiTXYuwLhlOYs4r6ujIrwW3SlZqGHcPRy+BlyZmMMRwuvIcxJEv/3/JB6Puvch2c2WyuI1OiKLEHfNndotn19e7Vfuz4bqxkSLV21TouXJBUBPmSX36e/shjURNcSiqG+V8+SKULcul7Ep7mo8I5/laoKFxae18KPPy2Jp48eiPyNYqNVlRJqyaIuX9537Fi/najRi6YvsQ0DsBYXXfY/AB/7C/90YsRZNmS1uxlSuRhebtPkzklD0wv+K3mor1Qbjx7bKOFNRLZ9575dh2bvFpRtcRNe3isDtPymLoYpaSTAKJja5tgQXSM5yd/q16M+Pwp2zbC1e/Sf8+WPRnTIfuVIwwcSRsJXKBdYvebuMc24MdVs/VdYluxrjwqv4cV7leX7vdcCnrLUvGWO+SsitaK21xphIf4Ax5kHgQYDW1lY6Ozvz0siBgQE6OztpPL+TNcArbxyl91T6z2o6d4TVwPYXn6Kv4TSx0ctsHr3MoRM9vNnZyZpLMTjpTY6xJnYE2r7B1DF8ZC+Dtb20mXKefu7F+Gu1F4+xEdi79WlOe9fZutPHGKmYwqtZ9r/yUg+3AvteeY4Tp6dSN3CEtwB7Tl1i6mA5s88e5FKvCMADPSN0XcP3a65eYTOGvgNbaQBe2XuA3hNV8debzu1jNfDKlucZrD3KbViumnJGj7zA8z/7KZu6d3Ni9js4mKYNdQMnpf3P/4irsUpWAtv2Hqa/K/t2N1xsxcmBbfu76U9xvqf2nmYdcPnIFq6WT+HFpyLM9B4z+uq4CXjzyjQOjfFdtp86z9zhfp5+8kkwJn7tAby+7WnqLh5llqng2dD7TOm7wHpg91OPcXa6FMTccPoow9Wt9J/qpf1SH0/97CfYWDnLD+1gOjC67e94ofIOrlSIGC4f6WfTyEUOnIcFsWq69+3g4NXEz2k8v4s1wM5j/Zy/2El9/yE2ALu3PsPZI1doPfk8NwIvvbqPoYOJGUcrTQtVR7Yzcvwg5bXz2fZ8RHX1EBupYeDIa+wN9Ld8pJ9Nex/j7KkT7F6ZKGRbzmxlBfDyjt0MHBzk1oqpDJupbPf+v+FCN2uBnc89wfnmiCLCHu7el/d8kRX9J9hjbuBMxPlb0TdIC3BuaJRdoddvrF1G476f8oJ3PqX9A2zyFiLDT32Fl4ZvxHqxV7UX32QjsOdQF2cG/PdaPniVmqE3ebmzE3N1hDsuD3D4VC9HOztZNTBE2egFXunslPcGDl1qop0YR7f8P1rOvsRI/VJ2PptZPaRg35f0DDCj/yzPdXay9uQRrsYqGOi5yOz+MzzT2UnTuVfk/j14it6e5O9mLDZUTqf+4lGGqmfy0gvb2EAtw0dfY3dnJyuOvk41NbzstaX9dD/zBs/x1JM/AxOj4nIft50/zIFFv0rXM89Fvn/V8BluAd546UfErl5hCdBfNw976mj8mgBYe+pNrsYq2dnZSdllyXLbtX0LzedeY2ZZDc8+/XTk+08/fQa3LHzq5d3YWET2nUdT4zupqOvldGcn9f0H2AC8uu15eo76weZL9j1Ga1ktz+49xcymu7jhja+x89Gvcr55DQBlVwa5/fIAV88e4Oknf+oHiyNj7aaulzkx+50J42VsxgMsGKzixMBshr3jy/pHaT53jIP/72FuAg7OvY9Fhx5hz6N/xkDtGjo7O1l48J9pM+U8s/8C9qD8X/XQADcDbzzzKN2zU2QUJn1Hz7EcOHxuhHZg64vPcLG+K+3/mKsj3DHYw+Gzwxzt7GRqbznrgN0//GvOTr+FtmOPI0tNw5uvPs/hi37Cy017/pWpVS0cOV/GDcCLT/4bwzWtvLX3DH22malXy+g9dojXOztpPblFxqt9p1lXXsfpXU8xMHtu3jRFJuRTeHUBXdZarzgR30OE1yljzCxrbbcxZhYQmXZhrX0YeBhgw4YNtqOjIy+N7OzspKOjA/ach52w9pY70wd5A3RNgV2w7saFsLRDUqOfgYU3rmHhxg441S4B5MO9sPhuEtr+Zjv1lwagtQl6GhJfGzwHWz/JTfNncNPN3vFdV2F2O1n3/8pleAGWtk1j6R0dsOcxeBmW3/5eeHMGdP0Ls0alPMPiDXey+MYs3z/Mtuk0jIiFYe3Nm/0CmQDHamEXrF2+TNx3z0Ns0duIHfgxd8y5DFcvMXf9u5i7Nk0bhi7Ay7/F8jlTxMKzG9ZvusvffDkbRjfB638CwxdY3/Fzqd2NFxbCKw9ROXIBZq9Nfw76lsGBbzKv45eZtyjN3wGUbYc3v0fHpltk1fpaP+yUl26Y0wi9A3B+SvLnDa+F7Z9hxaxq2OS99vIQ9fOX0TJzFRz5B+7YuFpcovu+BI3zKLvwJptqDog1ECQO6DlYvP5tDB3/V+Y21TA3/DkvH4KdsPquX/CySRfDNlixcBas74CX3oDX4a13vDN5/8rLP5FyHSM9sPr+zK7bA23UVpQxI/i3Z/fDc9DSt5uO296amOG56zTsgQ23bJYM0a6NVDYv9D+rZy7s+DyrF7bCmtSfH7/3Af76j6FxHss/+DmxwoU58wj0bKV55vzkPtUfhn99mo4Vc/zipQd+As8BGz9O9Za/4I7WPilmClKSYSssX3sLLAm8V98/w76j8v79J+FpaL9pHe0bO+DELOg7Ia/1HofnYOGKDdD3IgvsMbh4BO78Ih2bU/c3Zd9Hn4HuH9Fxxx2wexRmLKVp1mro+r5897uPy/27+Z7x3W/dK+CNo9TMXy+f+eYC6i9flOcH/wCmLvDbUrUX3vwnOm5eK1bcU3vgeVi8bjOLl6fo29VR2PrrLGupgCujUDWVKQvWw4ntiedqj4EW+awtP5CV7aobFsH+w9DXmPpaPVwGe78MlfXccefbx+isvMdNAGfbYBusXNoOqwLv/doXYf5b6XjbnXDlNvjy37K68ih0/La8fmYfPAsxO0LHmsWJNRZPvAJPX2buzT/P3BXh9r6ThKCR0afh2Se5qfo0VDWw6KN/Bl95guWjezhTv8n7/v8Q5qxN7NfVq7D90yxrvMKyTOedrQdhL7Svug2OfJu3rF4xdnzn+SNyja+6lfZ1HTByM+z8fVY0DkFHBzz2XaibgamsZf5Uw/xgW175JCy+nRtWvBXe+Do3r7lBrIBbrlAzbxEcPU1Ncz0zOzpgy35vvHo7dC1jTvUQ++vrs59Tc0jeXI3W2pPAMWOMKypzF7AXeBx4wDv2APD9fLUhK1LFrUQRdjWGXQc1zWKeHzgpqc5BXPX64D6N8fdtlNVNLmK8yivl/1y5iPjG0Qvjpvmm895sf60xXiDmYvcdJhVQ9SbNkYt+35bdI48uIH3GGGK3plHet7cr4BYeZyZmWTkseTuWWOqAefArhkP6vwPp/+8dE3P5WDhXbNw8HnI1jgwnCg1HdYO0w53Lq1f9rYzi5Qi8c9B7XGquLewQIeQyEl1GY0MbIxUN0a7GnoNSPsHF7oVjvNz/RF2XLcv8PQeDcSPpiAqodrFnI4PJ5RrC5TZ+6XsSe+Rw7mPnIhuL49tl65e3fiJadIHvLo/qc7uL8wpYTI5tlVimO78g91ywurYbL1LFeFnrfx/BrEYX2+dcKJX14rpz29a4eLNsqW7wkoUGEmO8QNrhdoxIl4iSDucKdOEEwaDvi2cSxXu4kKwrC1KfJiQgVibhEucP+3uD1janDq4n4Gq8PCgxWKniu8B3NWab+R13NYbiOU/v8e+N8iovOSCQaRzcoSNcTNaVfhgrdhJkrrGjEl83/1YJXVjxQdj/Y8pH+mVM6N6R/F6xmBdgvyfybSNx5yu+r2gGMV7u3DpXY0W1iCfXx1N7pB0NcxNjvPq6ZRxr25g87l0ekPsiwdXoff+V9dEJSkUg31mNnwK+ZYzZBawB/gD4I+Dtxpj9wN3e78UnKm4oFfFqz72Jj8EYr/im2e2J/+sCHsOVq0Eu+NppfqaZtd5gkUGbolh8N+z9vtzsPQdl4Kyqj7ep6fwu+btrjfECf5KG9OUkXN/m3SKD4P4nZIIK7i+XioY2P8bLxNLHXI3FnV9kz/LPpp5owS+iCqkD64NkmsYfF17eNeKEl4v/GxmMFl7glZTwBo7hCzKw1rb41+3QeRlQL56WrMybf0MEyN7H5HU3gDXMZaRianRWo5u8XNxNZa3EDznBe/o1uYaiMmuD5zFc5DcVtdOSA6GDgnBfqJhjVJ2zYFxdZZ1cG5luG/Ti/5JMU7cxehQuwzRKeDUvlOs/GOfVtUUWE9UN8NZfk3hGF08TLorsqGmWwPBL/cmLi/IafzIbccKrzhczFXUwZ11m/Q3j2jF0IUJ49chEV9OU+pocC5ds4jwJ9TP8BVh4D9RwlqubnIPZq1G4Qq2uaryLeQzWkwoILz+4fjC69mCQYMZlNsTv80CM1/HtEosVFDtRwsIRFgldW2Qcd6VT0uEE49A5P9t11Yfg6gjL3vg6/ODTEjcYZZmacZMIRGslpvCFb8ATX5SfXd9N/vvBHrmH3LWUybZBrjZccO6Yu1GseiPDEivauiL5+4kn7rzFnxuHLkhW65XhQIxXIKsxViEid9pi6O0iNhpIUCoCeRVe1tod1toN1tpV1tr3W2vPW2t7rLV3WWuXWGvvttZmmXqUJ4bOy+CWyeBSFRJebuKsDli8HM1h4dUqgct9x6OFQ3CPxZFBGYjHY/ECmXQv9UmmyLmDflHIxnlgYtRfPCxZOOMJ3g8zNbAaTion4QWtXr7o962+VW4ce9XLAApl8EXR0CZBluk26c2UpgWcnX5rBp/pBYumquE1Htz3E866aV7kWbyGUl+H05fJgOisXa5twZWfWzFPnS3iu3mR1BgDWSl65VFGKhqk7EOYngPJLqWaZr/Gk6vOH9m+pX6bMi3kWdvkW3ocThDOXCXCK/ha1F6WYepbMwuu7+2S1Pz1D0RvV+VIFVwPIvqW3C2LiKHzcm66tvnCc80viiV7/xPye8rgem9iHzrnp8o7N1OUxaui1rcUz7t57BIzqXD3f98JyQ6saQ5YOXtEuAcnx2xZsEksoa7oc910OYeD5+S7SLB4BT4X/HOYzuIFshA4d1AsR054Yf0xevSKCNawxctlNaYTXq4mVrbCq6JWFojBYskuKLxtvX/MFYd217i7f8urkwPBj22R6yqT8j/1AbHqsl1nroL2O2g+twN2/7NkIc7flPy/rSu8saRbLOY/+jxs+Ut5/ujHfU+KY7BHzl252xEgE4uXS9oInNu2t8gC47XHRUTNuEm+n/4T/jZ3x7eJkJq1KnHcC+65GQ6ur5ri10LEUjM0zgLLOSLfFq+Jw+D5zKsxV1TLBZbK1Rg0SSdZvLyboedACuE1zV8NploZZ0rbeph7s2S2nd3vFxUtrxRrCEBTDtyM4LshymuSJ4C4q3FI+ma8An9u1TdWRqPDrXyy2SvuWnGm87FcjdkQ5Wosq5IU/YFTMuCES0k42t4i10XP/kANpBZ/ohw6768kp84Wcbrqw1Jcs7dLJqaGNjDGs3iFhFd4r0uHq14/dF4yj1KV7KhukGtr7lszrw1XO036HKzw7UTlmo+K2A5mWLnsPjfIRzFlZmbV63d8W6wiGx9M/3dxV2MKcbbxQWn/tkcku/dSr+9OqqqX79xlo0ZtFwaJ1p5XvyuFOZ0ADlauD7oaXSylq481Htz44mqOBS1eQ+c84TWG8EnHjBvhk1v893CLGHdOa9MIr/5Tsjgca2HW3O5dPzYgvPA9GXH3rrN4eZmrcYtXiuKpINfx1Dlji7+o/6usTwwlOLZVSvoEx6+GNhFnrh5V/0np8/RlicJr4LTce5m68N1cU9Pk10g0Bh54nGc2fxe+0C1Z4FHWfGedPPgkdP4RLHmH/P2ntgMmvu1aHLeBuBvrMymg2t8t93Dwu3B9cyEorcvl+7FXfaHWvUPaV17lLxqGL/iWxSonvAILWzfmeuNazVDAnVsEVHg5oqrWp6Nqqn8zu4HUnVw3CVY3JL+nM/9eGU69IbSbdK5VeAHc8utysw6dS5xMXexZLtyM4AuvqJVj3NU4mJg+7kzcqfZoDNPQJuept6uAwstVQ8+h8HL1fYLCq2qKH/83MphaVDixemxLYLuVlsQtZ5zwcu6Ilb8gj69+z99MGrhcOVVWl8FCwG4j9bDwqmkSC0W3FxeYyuIF8IvfgXv+JM0XECLVPoUVdbJNFSTuHTcyKAI/nbBrmJsYN5OKs2+I4B1rk/h0Fi8QAdS+WSako172XdCd1Nzu1/W71CcWsLC4dt/Dmy/K97zqQ/5rwcr1ceFVJ+1+4F/GFo7piAsvr33hGC9X5DJXuHvJFceMdDV6jpCBk2O7GSFxgdu8MLAQuSCPTtS482fK5B6LC68xwhbu/zbc+cWx2xGmaoovCFzh1HBMVbBUDsj9O3VW4h60kOhiywT3Pc+/LXvvgLOk/vAh8dC864/kfmucC8vfD9v/LlFQDp1LFF4ZuRq7Zd4I3scNbWJdPfqcF4KyzB/HervkOwxa3CtqZRuqofO+0HIWL7eQcyUmQMq/3PunDNSPI0kkh6jwcmRrRQlWe467Gr0BzImtsLULEs2/USIlKLzcivBagt9v+DlfXAUnU9e2XAkvNzBHicmyCjENjwwmxnTMvRmW3Qs3viezz3AD1Kk917bFUTbELV65dDWGKlq7FXf9DBk8Lp5NE+O1WK6zri2hDYanykDlhGmw7dMWSRHgV/8pQXiNVHjXa7jIrvucIM7iFRdea1L3b+YK30WbCZHC66xYf6fO9t2NjnSuWEfTfFkhB4vNRnHhzczugbEsXgC3fFJcIk99WcaS4LZVTe0Bi1evX2w5iLumtzws53LFB/3XymtkArQ2ILy8NrVvHn/8Ffjj1rmA8HJj4cApiRe8FldjmLjFywmvgMWrsl4m0mCMV30GwisY0jFtUbLFy13jQXdhRY3nahxI72oEsbyMR3y6jbJB+jR0Tu6PIGHh1e8JkuZF/h60IIutWEX6ey/82at/ETb8avbtrm2Wc36pT67r4LUcDGFxhF2NmVq8ojLK3YJ82mI5R27+u3BMvo+h8/53YIyEnQxdCFi8psiiLRhc785vZR1s/BjDNcUtH6rCyzF0PrtYpwThlcLiFY7vgpDwihjEa1vETXHlklQbrqxPb10Yi1iZ7BOIEZO/w7UtFxmN4A/MqQawyloJdnSV1t2x+789dvkOh6uWfXmgcBav6ctI2pj3WokKrncWL5ABOJWrMRaTFW/Xy358lrMgVjf6rsaqhsRzsepDUmhz4GR8oB/xanslCJ6oTcbBi/HyYo8a5uVW+IazJsG7TrwJefHdcOwl3+IzksYV62icB9jkitdhzh/NUHiNYfECWPx2qaQ9cDI5Dqe5XSbd4d7kfRod7js9f0TEVNC954rDXhlOdDXmAhegHHQ1lpXLGHf6NXHz5NLi5YTXqQiLV3ij7P6Tmbk5G+cDRkICqhuSNx13MUnBivsVtd5ejX1jC6/xEiyWHAwBCBK06IBvCZq2WL57d16OPOMXTs0EY+Dn/xwW3zW+ts9ZJy7W23838XgwhMUlLwyOx+J1IvrcOoues7q5avi9x/yFXzDUwe3aERXj5bbnytf5HScqvBzjsXjFzdi9ibFN6Sxe1Y3+5qqRrkZvshnskSypebeMP2jWsfFB+OTWxGBn17axXCyZ4gbmVBd4Ra3vahyv9SiYyVMo4bXkHfCpbdEieryEs50uD8hE7FwDdjS9BaNto0yI5w7K9eSuDzcA9R1PHtyXf8AvxBgXXp6lIyy8qhuThZVLz+/eIUGtuSTS4tXjH2+cKxOQe31kcOzJx4mp4F6Ju/8v/PS/xX+NjV4WkZTJPZCJxSsWE9c+JLuT3P127nDC1jUJVDX452jlhxJfC1oSglmNucCJwKDwAvn+XUmB8ZaSiCIc4xWuBedEvrVicctEeFVUyzXvFgxJ5VVcNm9gDKmo9SxhNo/Cq96/z+PB5KF7s266xHheeFOEzMAp39UIck/2HJSg8ky9A7ngvV+Hjz0ZHf92y29ICMvrP5CF0OWB7Cxebv/PqOsqHvvrWQYra+Va7O2S8SdWnhieUtMYHeOFzSx5ogio8AK5CIbOZTeZNy+UvQlHR/x9Gh210+D2z0hQc5hYzJ9gU7kaQfai6tk/vk1vwxjj7aMXYNHbOD77Hsk4ygXVjXLTpbIIOOE1eDZ5oM2U+pn+xFQo4RXPhMkhFTXiSoq7Gr0VdzCAP53wmvsWwML+H4fiY5pTC6/66bDobfI8ravRS8cPu8FqmkUQnjuUfi/M8eBieIJ1twZ7/OskHq/jWS8ycTXG3ROB7KvtfwfPfz2+Sq+6dDrxb9OxsEPqfI0Vj7j6fnjLxxLjs8AX7ucPe6VkIoRXLOZPXuEJ1gnNK5dkJW/KxCWXC8rKpRSAyyAMCi8X95VL4VVeKePFpV5ZhEZtneSsg1eGMw9q73gIbv2UPI+XyHDC65h8ZnDMrajx9zLMlfUwTDC4PiqLD+S8N8wRYXHxjNxnU2bBNC8OqeeAt8ei8eM1C0Ftc+r4uhveLYubF77h35e102TcKK8e2+I1fEHiS6NcjXPWy6bZwb66zM/unTD9xsSFV9ziFYrxArlXLmfgSi4wKrxABMHo5ewKcrbfLivP49uTi6EaA3f9vmz8GUVceKWxeO15TB7Hs+ltJlQ3sH/pJ3J3QRojsTipKltX1oqFcLh3/BavsnLf7Fwo4ZUPjEl0QVzql8Ei6IZOJyzmbACMDHhBEesGoN7j0TFW639FJuvpUtM42tV4MFpoBi1gmcaYZEp1g18c1xF0Sccz3bwB/srQ2K7GqbNlZXzhqH/s7D6Jk/KOVQ874ZWBxauuRYq0Ru3jGKSiBt79p8lWNGdtdhavVAuUlqUS2xW2rAXT9C9flOsl06zRTHBCpaLWn9SC8VDpNpMfD24MqJseEevmuRrjNbwyFF7rfhluuFeel5WLBTFo8XKxVI6KWr+Q67XUBExH1VRfEPR1Aya6P65GYdAdWdMk7vae/ZLlOv+2zOp3FYJYGdz8a3DsRVkAQvS+oqlwtcqiBH1ZObzrDxO9DC5Z5sSO5NCb6kYY6k2M8YoLrwF/fC0hVHhBdlXrHa72yZGnvZiNLASMs2xErXrdgPT6D2TguJb4rkLzKz+Au78U/VpFrV81fbwWL/AHnoksvMDLig1lNda1iCUM0guL6ql+vF5YePV7wdBTI4TXje+Bzx2JD/xXyuvE4uCyI4MbqYcJLkrycU0GiyReHhRx5Sb+8Vi8YmVyrTiL13CfWAJBtmQBaoY8a0cu4/dSUTVF7u3zh/3g+ij+3WPwnq8mHy8PWbxy5WZ0uPjW4H3lvv9YeWLJh1zgFp9RY4ETXgNZCq+k92lKFF6NIeFVWSv3CuTZ1egsXic8t2JE6EjDPBkfw1axaYvhjX8Tq9eq+/LTxvGy9pdkHHvKy2B214tLWkhHtrshNMwVATp4NtninirGC2SRMzKYP2E9TlR4wfiEV9008UEffibZ1TgW6VyN7uK91CvbPGRaDb0UKK9M3d6KWj+9/1oG8bjwKlBWY76omuIPFC6rKlbmfzfpalQBtG2Qx6D1sKYpUDw1RVZhcMJ2gczO1XjukDxGWbzcvTFldm5LazicKwESy2RAssXLlZMYi8b5EjwPUsfOceZ1wLN4xSpy60ZLh8tsTBVcD3IPRU3MceHlLF4ZFBzOBrcIDI6B7nn9zGsrVhxF3OIVJbycy7zb//zxUBMQXq5+XZCKWimdAvkTXs7V6GKaUiUpNLTJ626h4OLApi0S92NZJdz0vvy0cbxUTZHCw33egsnNXaksXr1dsPWvJCYsvF3QWLhaXpBsca9plLF06IIsEsqrJKsR/N0r1NVYgoxHeIG4AV09pWwUtXMpRf1PdYMffJ+L+K5SoaLW3/LkWkozuFXrhLd4ea7G0RH5Xty14K6NsSw6LgC1NmTxcmTqGgpmkKXKaARf/OQ6vsvR0OYL83Dqf7A4LKTeyzJM4zx/Ijv7hjzGysXlCFQPn5LrKdeiIhXN7emD69MRz2rMk8UrSni57z+XGY2OoKsxTO00mWQ9gZxRHa8oXO254V5ZyCYJr8A1lK6A6rVQNUVitq4M+9mKUTS0AVYC6E2Zv7hxi6Al7yjNMW/jx/2421QWr0v9ktTy9fWyTdGjH5dwCMjC4uWdOxNLLrjtvpe+474L3t0fzmqqwqsEGa/war9dJs1zh7ITXm5SjErJN8YfjPIV31UMgiv0axFeLlbmWtyVpYBbCTs3hBsY3IA7VgzTvJvlMTgpBa2AqSxeYeoihFdzhMWrrgUwMHttZu+bLY1zvcyk/sAG0d45rqiW7yMuvDKI8QKJs7p4WlyXZ94Qq0HbRnmOZ/HKVR27TGhq96wDNjsLOSSm6Y8M5j5mJZ3wyodFMO5qTCG8QOp8VdSNf9IMxjxCdIyXI5/lJECu6/4TYwgvZCFf3+p7Dlq8LbhWlpib0dE4Vyxxpsy/dsLB9d/5d/DMn0qow+2fkX1jX/ifcp7dgiKTzwHZCzZs7XXlUC4cS6zXBQGLV2nFeJUXuwElgRvosxVe828FDFkPpKs/IoNAqtiFumki6Fw67fVAcHVZNy31343Fyvu8fQALOGHmg6op4lqLCy9vYIhbvMZwNbYsgV/8J5h/i38sYRuSDIVXbYtfGye4kXqYmib46Pcy364kW+JFJI8nuxrBLzEAmZWTAF9U9R4TK9e0xbJa3vUdsNYTXhns15krgsHC2cacJFi8BnIfc+Umr0iLV44D6yH9xtNuQXpq77VtVRQXXp4lNa3wylMMkBPIg+dkgZPqu4xn4R6VrD7H0nvgvkfgxvfmp3254N4vS7yXSzypqE10NZ7aA2t+Cd7/Db8A8Et/nt385s5dVHypi0/s7fIXEHHh5bmrS8zipcILZHUxdU72wqumSWoade/M7satrIOl70j9+k3vl5T3QrlACoHzucfK/UF+XO9TA8vuyUmTiopzNY7X4gXJ15C7fqumZj7Q1LWI0Ok7IRvTLnl76r9dcndm7zke4kUkjwVcjQELXm2TH1x/JVNXo6vldVTcVjNXSUbnpT44d4jKkd7CCvhgXb9sLV4ups0VUM21pS7S4uV9/4W2eDnLbV+XZPKNl5pmsaI6d3NaV2OeLV49XoxhKiEZFGTB77usXLboKWXqWhKLtFZU+4ska+UcuEWUMfDOP5DrOBtRXTdddmFZEVFOw12z/d3+gjMuvJyrsbSC61V4gaQhr/vl8f3vgttFeGU7kKZj82dy916lghvkaltymwY/UamaKkH1LtU8LrwyjPGKwg1AmboZQSwOw73wo89LvNld/zn7z80FcYvXMbEMhAV6sKjmyGBmwtRZEc7uk+KgK+/zXTcuBb6QrsacWLyGxXVaCFeju45yWTzY4c5NONMQEq1gmWwXlIqaJokVO71X4mbD7+VcVmWVmbu8ssVZj724wpRbL1XUeNvFnSlcske+CAbXjwx5pZoa/ddjMXjPV7J7T2PgI9+Kfi0+Tlj/vnDjg8Z4Xae0b5bHEjuxJYcb5HK55+FEpsrbw81tOxUPrvcsAZlk7YVxg1s2riE3ye15FG77rdR12PLNFK84bm+XWOBcMcZ4O72imm5AHyvrE2SiLauCgz+TCXj6MokRAdj/hDwWUnjVTfctv1kH1wcqgl8eKExWY9N8+MSzcEMeqqXPXAkffxra70h+LSi8rkWEuL507xJLSNiD4CbnfNZ4cvOCy6pNl6jgFh/5SGYoJMHgerc5+bV4OcYieM06oZtk8Sqt+VmF17XSvln821EDiOLjJpyJHhSfK9xAEI5BWNghG9uGN9LNhLjFKwvh5c5Hw1zY9DvZf2aucO7+3i5/37cgzuLlBvRMLF6xmFhUjjwrv7csE2Fb3eAfK0QNL4cxvvUoW+EVD64fzm9WYzjhZ+bK/IU8zFodbf2urBPBDOPPaAT/fji1Jzm+CwL7b+ZxUq50wstZvNIJL88VmssNyYtB0OLlttXL9nrPhqA1zX3fsTJZvOZ7Z4JxosLrWqmogfd9Y+IHe+cbN8ip8BLcYO9qFbmBoa5FNrYdz8Ra3SiCJZugVWfhetcf5t6Kki2NXnXqYNV6R02TrJ7dBtGZumIb50u1ehPzt0JqWQajlxiNVeanJlk6XFbueF2Nl/rh6kjuhVdzO2Ci95ctNMb4AnC8NbzAF15XhqIrvrvFYD7jf+Kuxv0iJtPFETtxeC0JBaVAlMUrKI5yTVmFP34GE4MqC1CnbZxojJdSGNxEoa5GIW7xOpH4+7UQi8FvvuJPKJkwazV89mBpCOKGNjj6gmRHzVyZ+Fpts7gL3Qo2Y+HlYonm+5mQ05dC1xaGq2dQV+h4w7jFK1vh5bXdJRhkc44zoXU5/KdD0SVuikHtNLEG58LiBcWzeMXLSfSJ6E53vbl4t3xkkRaSSItXY34/s7rRc8EHhVedxItW1JVcIXIVXkphUItXIs4k7vZmy5UpfDwm/VI5Jw1tfhHEKFcj+NsKZSq8nCXaxXYFng9Xt5Jj+TI26/+9bA+TbfJErMzb3smruZZrixeUjuiC3GRUBvsTafHyLLz5rPEUFMhjuRBXfFASR6YtyV97CkFFjQgva/0Y1nxavEBEdl9XyOLlrGClZe0CFV5KoXCDXK7rD01UqgLCq3LK9VU6ZLw0zJUq35d6k68TN4m6/RaztXhNX+ofa5FNwoerC+xmBKlEHrUlUyZU1ORXeJUSTnhfS1Zj0MoSJbycaz2fE3MsJgLg8sDYLsT6GXD77+avLYUimAhSiOB68IVdZeBcFkJYjxMd7ZXCEN4SZ7ITF17HS3JFVhSC7qCwFS5e28mzEGaa9eliqqbf6B+b4Vu8JhTlVX6Ns+tdeLkM0GsJyi4r98edqCSKQrgag+8/0V2ImRLcZaEQwfXgC6+qkKsRSnJ8VYuXUhjmrIf3fzOx0N5kxg0Gw70qRh3Bmk5ht1ftOF2Ns9fBz/+FFCWOf848uO8Ruk+UM07bU3EoDxSmvN6F162fkkLJ1xqDV9Mo8VVRte0qCmDxAt/lNdHrc2VKUHgNX4CqhvzHWDmLWqW6GhXFJxaDNfcXuxWlQ3AwKMGBoSgEJ8ewqzG4ES5kVk4CZOJe/ZHk48vfz5UznVk3saiUV/vlR3IdXF9qNM7LTamPmiav/EbE9RIXXnmuau6sMBO9PlemBHdZGLqQf2sXBHbtCGU1QslVrQcVXopSHIIrMxVeQlW9v79eOLi+ulFKQjhXYyZ7NV5vlFf7Ox1c7xavXDFlll8TLEz1VCnam+/kEnd/TxqLl3dvOotXTSGEV6M8hrMaw8dKBBVeilIMyiv9tGsVXj4Nc0V4hSfDWEzEV9ziU+SaY8UgKDZVeGXGu//Mr+UUproB/uNPYMaN0a/nispJJryCFq/h3vwH1oNv8ZogrkYNrleUYuEGhBI0hRcNF2AftniBX8sLxreX5USnXIVX1jS0+QkWUcxZl/9raTJbvIYu5L+UBMiOH8s/kLjlWaFi+MZBXoWXMeaIMeZVY8wOY8zL3rHVxpgXvOP/YozRWUeZnLgBoQRN4UVjxo0ivsoqkl+rCQTcj2cvy4lOcCNnFV4Th4a2xAK+1zsJFq8LhbF4NS+E+/4m2io82YSXx9ustWustRu83/8SeMhauxJ4FPhsAdqgKKVHCZvCi8bmz8KDndGvuczGWIWUCphsOItXrBzKKovbFiVzNn8GPvazYreicBTD4hXFJBdeYZYCT3vPfwx8sAhtUJTi41yMJTgwFI2K6tTBzs7iNRnju8AXXhV1115mQSkcFTWlsztEIXAWr+Fe2SezEFmNUZSw8Mr3stECTxhjLPAX1tqHgT3A+4DHgPuAiE20wBjzIPAgQGtrK52dnXlp4MDAQN7eu9SZzH2H4vd/xcAlWoB9R7s5caXw7Sh2/7NlUc9F5gKXbIwXctDuidb/ZWcvMAu4ZMuvuf8Tre+5Rvufv/5XDZ/hFuDwrhdoB/YdO8OJInzXLWeOsAJ4df9Res4nfn6xz3++hdcma+1xY8wM4MfGmNeBXwW+Zoz5feBx4HLUP3oi7WGADRs22I6Ojrw0sLOzk3y9d6kzmfsOJdD/nm9Bz1aWrlzP0lWFb0fR+58tsZeh6/tU1TXkpN0Trv8X/wVOQtWU5mtu94Tre47R/uex/xd74EVob6mGI7B01UaWrszTZ6Xj4FXYAys33AYLNiW8VOzzn1dXo7X2uPd4Gonn2mitfd1a+w5r7Xrg28DBfLZBUUqWKo3xyopadTUC0cVAFaVUcFmi/SflsRDB9VEsuB3u/VOYe3NxPj8NeRNexpg6Y8wU9xx4B7Dbs35hjIkBXwS+ma82KEpJEy8nocIrI1ytnvJJkh0WJi68NAtWKWHcdTrgCa9iBdeXVcDGj5VkIk4+LV6twLPGmJ3AFuAH1tofAvcbY/YBrwMngL/JYxsUpXRR4ZUdGlwvj1pKQillYjHZLaDYFq8SJm9S0Fp7CFgdcfyrwFfz9bmKMmHQrMbsiLsaJ2ENL/DT9Cer8FQmDhXVAeFVpKzGEkYr1ytKsViwCZa9G6a2FbslE4O4xUtdjYpS0pTXgB2V58VyNZYwpef8VJTJQutyuP8fit2KiYMG18ujuhqVUqciUHMuaheKSY5avBRFmRhU1MhKetIH109S4alMHFwRVbV2RaIWL0VRJg5v+zzMWV/sVhQHt1ejWryUUsdZvDSwPhIVXoqiTBxu+81it6B4uKQCjfFSSh21eKVFXY2KoigTAWfxmqwxbsrEQS1eaVHhpSiKMhFwVgR1NSqljlscaCmJSFR4KYqiTASa5sOU2TDjpmK3RFHS4xJB1NUYicZ4KYqiTATqZ8DvvlbsVijK2KirMS1q8VIURVEUJXdocH1aVHgpiqIoipI71OKVFhVeiqIoiqLkDrV4pUWFl6IoiqIouSNu8dKsxihUeCmKoiiKkjucxUtdjZGo8FIURVEUJXc0tIn4mtJa7JaUJCq8FEVRFEXJHTf8HHx6L9Q0FbslJYkKL0VRFEVRckcsBrXNxW5FyaLCS1EURVEUpUCo8FIURVEURSkQKrwURVEURVEKhAovRVEURVGUAqHCS1EURVEUpUCo8FIURVEURSkQKrwURVEURVEKhAovRVEURVGUAqHCS1EURVEUpUCo8FIURVEURSkQxlpb7DaMiTHmDHA0T2/fApzN03uXOpO576D91/5P3v5P5r6D9l/7n//+z7fWTo96YUIIr3xijHnZWruh2O0oBpO576D91/5P3v5P5r6D9l/7X9z+q6tRURRFURSlQKjwUhRFURRFKRAqvODhYjegiEzmvoP2X/s/eZnMfQftv/a/iEz6GC9FURRFUZRCoRYvRVEURVGUAjFphZcx5l3GmDeMMQeMMQ8Vuz35xhgz1xjzpDFmrzFmjzHmt7zjXzLGHDfG7PB+7i12W/OFMeaIMeZVr58ve8eajTE/Nsbs9x6bit3OXGOMWRY4vzuMMX3GmN++ns+9MeavjTGnjTG7A8ciz7URvuaNBbuMMeuK1/LckKL/XzbGvO718VFjTKN3fIExZihwHXyzaA3PESn6n/J6N8b8nnf+3zDGvLM4rc4NKfr+nUC/jxhjdnjHr8dzn2quK53731o76X6AMuAgsBCoBHYCNxW7XXnu8yxgnfd8CrAPuAn4EvCZYrevQN/BEaAldOxPgIe85w8Bf1zsdub5OygDTgLzr+dzD2wG1gG7xzrXwL3AvwEGuBl4qdjtz1P/3wGUe8//OND/BcG/ux5+UvQ/8nr3xsGdQBXQ7s0NZcXuQy77Hnr9z4D/fB2f+1RzXcnc/5PV4rUROGCtPWStvQz8I/C+Ircpr1hru621273n/cBrwJzitqokeB/wiPf8EeD9xWtKQbgLOGitzVdB4pLAWvs0cC50ONW5fh/wd1Z4EWg0xswqSEPzRFT/rbVPWGuveL++CLQVvGEFIsX5T8X7gH+01l6y1h4GDiBzxIQkXd+NMQb4EPDtgjaqgKSZ60rm/p+swmsOcCzwexeTSIQYYxYAa4GXvEOf9Eysf309utoCWOAJY8w2Y8yD3rFWa2239/wk0FqcphWMj5A46E6Wcw+pz/VkHA9+FVnlO9qNMa8YY54yxtxerEYVgKjrfTKd/9uBU9ba/YFj1+25D811JXP/T1bhNWkxxtQD/wz8trW2D/hzYBGwBuhGzNDXK5usteuAe4DfMMZsDr5oxe583ab5GmMqgfcC/+QdmkznPoHr/VynwxjzBeAK8C3vUDcwz1q7Fvg08A/GmKnFal8embTXe4D7SVx4XbfnPmKui1Ps+3+yCq/jwNzA723esesaY0wFciF+y1r7fwGstaestaPW2qvA/2YCm9jHwlp73Hs8DTyK9PWUMyt7j6eL18K8cw+w3Vp7CibXufdIda4nzXhgjPkV4OeAj3qTD56Lrcd7vg2JcVpatEbmiTTX+6Q4/8aYcuADwHfcsev13EfNdZTQ/T9ZhddWYIkxpt2zAnwEeLzIbcornm//r4DXrLX/I3A86Mv+eWB3+H+vB4wxdcaYKe45Emi8GznvD3h/9gDw/eK0sCAkrHYny7kPkOpcPw78spfddDPQG3BJXDcYY94F/CfgvdbawcDx6caYMu/5QmAJcKg4rcwfaa73x4GPGGOqjDHtSP+3FLp9BeBu4HVrbZc7cD2e+1RzHaV0/xcz+6CYP0gmwz5E4X+h2O0pQH83IabVXcAO7+de4P8Ar3rHHwdmFbuteer/QiRzaSewx51zYBrwU2A/8BOgudhtzVP/64AeoCFw7Lo994jA7AZGkJiN/5DqXCPZTN/wxoJXgQ3Fbn+e+n8AiWVx9/83vb/9oHdP7AC2A+8pdvvz1P+U1zvwBe/8vwHcU+z257rv3vG/BT4R+tvr8dynmutK5v7XyvWKoiiKoigFYrK6GhVFURRFUQqOCi9FURRFUZQCocJLURRFURSlQKjwUhRFURRFKRAqvBRFURRFUQqECi9FUSYkxphRY8yOwM9DOXzvBcaY672umaIoRaC82A1QFEUZJ0PW2jXFboSiKEo2qMVLUZTrCmPMEWPMnxhjXjXGbDHGLPaOLzDG/MzbJPmnxph53vFWY8yjxpid3s+t3luVGWP+tzFmjzHmCWNMjff3v2mM2eu9zz8WqZuKokxQVHgpijJRqQm5Gj8ceK3XWrsS+J/AV7xjXwcesdauQjaI/pp3/GvAU9ba1cA6pJI3yPYp37DWLgcuIFW+AR4C1nrv84n8dE1RlOsVrVyvKMqExBgzYK2tjzh+BLjTWnvI2yz3pLV2mjHmLLJNzIh3vNta22KMOQO0WWsvBd5jAfBja+0S7/fPARXW2v9ujPkhMAA8BjxmrR3Ic1cVRbmOUIuXoijXIzbF82y4FHg+ih8T+25kb7d1wFZjjMbKKoqSMSq8FEW5Hvlw4PEF7/nzwEe85x8FnvGe/xT4NQBjTJkxpiHVmxpjYsBca+2TwOeABiDJ6qYoipIKXakpijJRqTHG7Aj8/kNrrSsp0WSM2YVYre73jn0K+BtjzGeBM8C/947/FvCwMeY/IJatXwO6U3xmGfD3njgzwNestRdy1B9FUSYBGuOlKMp1hRfjtcFae7bYbVEURQmjrkZFURRFUZQCoRYvRVEURVGUAqEWL0VRFEVRlAKhwktRFEVRFKVAqPBSFEVRFEUpECq8FEVRFEVRCoQKL0VRFEVRlAKhwktRFEVRFKVA/H/BhhUd2lDiewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "num_epochs = 200\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # 确保模型在训练模式\n",
    "    correct_train = 0\n",
    "    total_train = 0\n",
    "    for inputs, targets in train_loader:\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, targets)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total_train += targets.size(0)\n",
    "        correct_train += (predicted == targets).sum().item()\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # 计算训练准确率\n",
    "    train_accuracy = 100 * correct_train / total_train\n",
    "    train_acc.append(train_accuracy)\n",
    "    \n",
    "    # 测试模型\n",
    "    model.eval()  # 设置模型为评估模式\n",
    "    correct_test = 0\n",
    "    total_test = 0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in DataLoader(TensorDataset(X_test_tensor, y_test_tensor), batch_size=64):\n",
    "            outputs = model(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total_test += labels.size(0)\n",
    "            correct_test += (predicted == labels).sum().item()\n",
    "            all_preds.extend(predicted.numpy())\n",
    "            all_labels.extend(labels.numpy())\n",
    "\n",
    "    # 计算测试准确率\n",
    "    test_accuracy = 100 * correct_test / total_test\n",
    "    test_acc.append(test_accuracy)\n",
    "\n",
    "    # 每个epoch打印训练和测试的损失和准确率\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, Train Acc: {train_accuracy:.2f}%, Test Acc: {test_accuracy:.2f}%')\n",
    "\n",
    "# 绘制训练和测试准确率图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(range(1, num_epochs+1), train_acc, label='Train Accuracy')\n",
    "plt.plot(range(1, num_epochs+1), test_acc, label='Test Accuracy')\n",
    "plt.title('Accuracy over epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 计算混淆矩阵\n",
    "cm = confusion_matrix(all_labels, all_preds)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887c8fc3-72f0-4ee9-9c39-a8eab8accee0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
